Anticipating Future Behavior Using Kill Chains

ABSTRACT

A system, method, and computer-readable medium are disclosed for performing a security operation. The security operation includes: monitoring an entity, the monitoring observing at least one electronically-observable data source; deriving an observable based upon the monitoring of the electronically-observable data source; identifying a security related activity of the entity, the security related activity being based upon the observable derived from the electronic data source, the security related activity being of analytic utility; associating the security related activity with a phase of a cyber kill chain; and, performing a security operation on the security related activity via a security system, the security operation disrupting performance of the phase of the cyber kill chain.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for using kill chains to anticipate futurebehavior of a user entity.

Description of the Related Art

Users interact with physical, system, data, and services resources ofall kinds, as well as each other, on a daily basis. Each of theseinteractions, whether accidental or intended, poses some degree ofsecurity risk. However, not all behavior poses the same risk.Furthermore, determining the extent of risk corresponding to individualevents can be difficult. In particular, ensuring that an entity is whothey claim to be can be challenging.

As an example, a first user may attempt to pose as a second user to gainaccess to certain confidential information. In this example, the firstuser may be prevented from accessing the confidential information if itcan be determined that they are illegitimately posing as the seconduser. More particularly, access to the confidential information may beprevented if the identity of the first user is resolved prior to theconfidential information actually being accessed. Likewise, the firstuser's access to the confidential information may be prevented if theiridentity cannot be resolved to the identity of the second user.

SUMMARY OF THE INVENTION

In one embodiment the invention relates to a computer-implementablemethod for performing a security operation, comprising: monitoring anentity, the monitoring observing at least one electronically-observabledata source; deriving an observable based upon the monitoring of theelectronically-observable data source; identifying a security relatedactivity of the entity, the security related activity being based uponthe observable derived from the electronic data source, the securityrelated activity being of analytic utility; associating the securityrelated activity with a phase of a cyber kill chain; and, performing asecurity operation on the security related activity via a securitysystem, the security operation disrupting performance of the phase ofthe cyber kill chain.

In another embodiment the invention relates to a system comprising: aprocessor; a data bus coupled to the processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: monitoring an entity, the monitoringobserving at least one electronically-observable data source; derivingan observable based upon the monitoring of the electronically-observabledata source; identifying a security related activity of the entity, thesecurity related activity being based upon the observable derived fromthe electronic data source, the security related activity being ofanalytic utility; associating the security related activity with a phaseof a cyber kill chain; and, performing a security operation on thesecurity related activity via a security system, the security operationdisrupting performance of the phase of the cyber kill chain.

In another embodiment the invention relates to a computer-readablestorage medium embodying computer program code, the computer programcode comprising computer executable instructions configured for:monitoring an entity, the monitoring observing at least oneelectronically-observable data source; deriving an observable based uponthe monitoring of the electronically-observable data source; identifyinga security related activity of the entity, the security related activitybeing based upon the observable derived from the electronic data source,the security related activity being of analytic utility; associating thesecurity related activity with a phase of a cyber kill chain; and,performing a security operation on the security related activity via asecurity system, the security operation disrupting performance of thephase of the cyber kill chain.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an edge device;

FIG. 3 is a simplified block diagram of an endpoint agent;

FIG. 4 is a simplified block diagram of a security analytics system;

FIG. 5 is a simplified block diagram of the operation of a securityanalytics system;

FIG. 6 shows a simplified block diagram of an entity behavior profile(EBP);

FIGS. 7a and 7b are a simplified block diagram of the operation of asecurity analytics system;

FIG. 8 is a simplified block diagram showing the mapping of an event toa security vulnerability scenario;

FIG. 9 is a simplified block diagram of the generation of a session anda corresponding session-based fingerprint;

FIG. 10 is a generalized flowchart of the performance of sessionfingerprint generation operations;

FIG. 11 is simplified block diagram of process flows associated with theoperation of an entity behavior catalog (EBC) system;

FIG. 12 is a table showing components of an EBP;

FIG. 13 is a activities table showing analytic utility actions occurringduring a session;

FIGS. 14a and 14b are a generalized flowchart of the performance of EBCoperations;

FIG. 15 shows a functional block diagram of the operation of an EBCsystem;

FIGS. 16a and 16b are a simplified block diagram showing referencearchitecture components of an EBC system;

FIG. 17 is a simplified block diagram showing the mapping of entitybehaviors to a risk use case scenario;

FIG. 18 is a simplified block diagram of an EBC system environment;

FIG. 19 shows a human-centric risk modeling framework;

FIG. 20 is a graphical representation of an ontology showing examplestressor contextual modifiers;

FIG. 21 shows a mapping of data sources to stressor contextualmodifiers;

FIG. 22 is a graphical representation of an ontology showing exampleorganizational contextual modifiers;

FIG. 23 shows security risk persona transitions associated with acorresponding outcome-oriented kill chain;

FIG. 24 shows concerning behaviors related to a contextual risk personaassociated with a corresponding single-phase kill chain;

FIGS. 25a and 25b show tables containing human-centric risk model dataused to generate a user entity risk score associated with a securityvulnerability scenario; and

FIG. 26 shows a user interface (UI) window implemented to graphicallydisplay a user entity risk score as it changes over time.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for using killchains to anticipate future behavior of a user entity.

Certain aspects of the invention reflect an appreciation that theexistence of any entity, whether it is an individual user, a group ofusers, an organization, a device, a system, a network, an account, adomain, an operation, a process, a software application, or a service,represents some degree of security risk. Certain aspects of theinvention likewise reflect an appreciation that observation of humanbehavior can often provide an indication of possible anomalous,abnormal, unexpected, or malicious behavior, any or all of which mayrepresent a security risk.

Likewise, various aspects of the invention reflect an appreciation thatcertain human behaviors can be characterized as concerning, and as such,their occurrence may likewise provide an indication of potentialsecurity risk. Various aspects of the invention likewise reflect anappreciation that certain human-centric factors, such as motivation,stressors, and organizational dynamics, often have an associated effecton human behavior. Certain aspects of the invention reflect anappreciation that the quantification of such factors can likewise beadvantageously implemented as modifiers to a behavioral risk scorevalue, resulting in a more accurate assessment of security risk.

Likewise, various aspects of the invention reflect an appreciation thatknown approaches to human-centric risk modeling have certain limitationsthat often pose challenges for security-related implementation. Forexample, the Critical Pathway Model (CPM), which has evolved over twentyyears of research into insider threat, is based upon retrospectiveexamination and qualitative scoring of case studies that highlighthigh-profile insider threat cases. As typically implemented, CPM is aframework comprising four loosely chronological categories that can beused to profile potential insider threat actors.

However, since CPM depends almost entirely on retrospective qualitativecoding of case studies, there is an increased risk of hindsight andconfirmation bias informing the outcomes and creation of its categories.Likewise, the specific categories used by CPM may not adequately addressother types of insider threat or other types of negative workplacebehaviors that increase organizational risks. In addition to theseissues, the terms and constructs described in the CPM are looselydefined, and offer no measures or metrics for determining the presenceof specific types of signals across each CPM category. For example,Concerning Behaviors noted by Shaw and Sellers (2015) range from“troublesome communications” to “security violations.”

As another example, Sociotechnical and Organizational Factors forInsider Threat (SOFIT) was developed using scenario-based surveysdistributed to subject matter experts. Constrained choices, and limiteddemographic and experience information associated with surveyrespondents, can impact the quality of the results, and by extension,the quality of the survey findings. Furthermore, the use of surveymethods is not the equivalent of understanding specific types of insiderrisk through use of realistic datasets and structured testing. WhileSOFIT provides a well-researched and comprehensive baseline forunderstanding a wide range of potential insider threat risks, itseffectiveness and accuracy in real-world scenarios is currentlyunproven.

Various aspects of the invention likewise reflect an appreciation thatcertain non-user entities, such as computing, communication, andsurveillance devices can be a source for telemetry associated withcertain events and entity behaviors. Likewise, various aspects of theinvention reflect an appreciation that certain accounts may be global,spanning multiple devices, such as a domain-level account allowing anentity access to multiple systems. Certain aspects of the inventionlikewise reflect an appreciation that a particular account may be sharedby multiple entities.

Accordingly, certain aspects of the invention reflect an appreciationthat a particular entity can be assigned a measure of risk according toits respective attributes, behaviors, associated behavioral models, andresultant inferences contained in an associated profile. As an example,a first profile may have an attribute that its corresponding entityworks in the human resource department, while a second profile may havean attribute that its corresponding entity is an email server. Tocontinue the example, the first profile may have an associated behaviorthat indicates its corresponding entity is not acting as they did theday before, while the second profile may have an associated behaviorthat indicates its corresponding entity is connecting to a suspicious IPaddress. To further continue the example, the first profile may have aresultant inference that its corresponding entity is likely to beleaving the company, while the second profile may have a resultantinference that there is a high probability its corresponding entity iscompromised. Accordingly, certain aspects of the invention reflect anappreciation that a catalog of such behaviors, and associated profiles,can assist in identifying entity behavior that may be of analyticutility. Likewise, certain aspects of the invention reflect anappreciation that such entity behavior of analytic utility may bedetermined to be anomalous, abnormal, unexpected, malicious, or somecombination thereof, as described in greater detail herein.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, without limitation, storage media such as a direct accessstorage device (e.g., a hard disk drive or solid state drive), asequential access storage device (e.g., a tape disk drive), opticalstorage device, random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), and/orflash memory; as well as communications media such as wires, opticalfibers, microwaves, radio waves, and other electromagnetic and/oroptical carriers; and/or any combination of the foregoing.

FIG. 1 is a generalized illustration of an information handling system100 that can be used to implement the system and method of the presentinvention. The information handling system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a storage system 106, and various other subsystems 108. In variousembodiments, the information handling system 100 also includes networkport 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information handlingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furtherincludes operating system (OS) 116 and in various embodiments may alsoinclude a security analytics system 118. In one embodiment, theinformation handling system 100 is able to download the securityanalytics system 118 from the service provider server 142. In anotherembodiment, the security analytics system 118 is provided as a servicefrom the service provider server 142.

In various embodiments, the security analytics system 118 performs asecurity analytics operation. In certain embodiments, the securityanalytics operation improves processor efficiency, and thus theefficiency of the information handling system 100, by facilitatingsecurity analytics functions. As will be appreciated, once theinformation handling system 100 is configured to perform the securityanalytics operation, the information handling system 100 becomes aspecialized computing device specifically configured to perform thesecurity analytics operation and is not a general purpose computingdevice. Moreover, the implementation of the security analytics system118 on the information handling system 100 improves the functionality ofthe information handling system 100 and provides a useful and concreteresult of performing security analytics functions to mitigate securityrisk.

In certain embodiments, the security analytics system 118 may beimplemented to include an entity behavior catalog (EBC) system 120, ahuman-centric risk modeling system 122, or both. In certain embodiments,the EBC system 120 may be implemented to catalog entity behavior, asdescribed in greater detail herein. In certain embodiments, thehuman-centric risk modeling system 122 may be implemented to perform asecurity analytics operation, as likewise described in greater detailherein.

FIG. 2 is a simplified block diagram of an edge device implemented inaccordance with an embodiment of the invention. As used herein, an edgedevice, such as the edge device 202 shown in FIG. 2, broadly refers to adevice providing an entry point into a network 140. Examples of suchedge devices 202 may include routers, routing switches, integratedaccess devices (IADs), multiplexers, wide-area network (WAN) accessdevices, and network security appliances. In certain embodiments, thenetwork 140 may be a private network (e.g., an enterprise network), asemi-public network (e.g., a service provider core network), or a publicnetwork (e.g., the Internet).

Skilled practitioners of the art will be aware that edge devices 202 areoften implemented as routers that provide authenticated access tofaster, more efficient backbone and core networks. Furthermore, currentindustry trends include making edge devices 202 more intelligent, whichallows core devices to operate at higher speed as they are not burdenedwith additional administrative overhead. Accordingly, such edge devices202 often include Quality of Service (QoS) and multi-service functionsto manage different types of traffic. Consequently, it is common todesign core networks with switches that use routing protocols such asOpen Shortest Path First (OSPF) or Multiprotocol Label Switching (MPLS)for reliability and scalability. Such approaches allow edge devices 202to have redundant links to the core network, which not only providesimproved reliability, but enables enhanced, flexible, and scalablesecurity capabilities as well.

In certain embodiments, the edge device 202 may be implemented toinclude a communications/services architecture 204, various pluggablecapabilities 212, a traffic router 210, and a pluggable hostingframework 208. In certain embodiments, the communications/servicesarchitecture 202 may be implemented to provide access to and fromvarious networks 140, cloud services 206, or a combination thereof. Incertain embodiments, the cloud services 206 may be provided by a cloudinfrastructure familiar to those of skill in the art. In certainembodiments, the edge device 202 may be implemented to provide supportfor a variety of generic services, such as directory integration,logging interfaces, update services, and bidirectional risk/contextflows associated with various analytics. In certain embodiments, theedge device 202 may be implemented to provide temporal information,described in greater detail herein, associated with the provision ofsuch services.

In certain embodiments, the edge device 202 may be implemented as ageneric device configured to host various network communications, dataprocessing, and security management capabilities. In certainembodiments, the pluggable hosting framework 208 may be implemented tohost such capabilities in the form of pluggable capabilities 212. Incertain embodiments, the pluggable capabilities 212 may includecapability ‘1’ 214 (e.g., basic firewall), capability ‘2’ 216 (e.g.,general web protection), capability ‘3’ 218 (e.g., data sanitization),and so forth through capability ‘n’ 220, which may include capabilitiesneeded for a particular operation, process, or requirement on anas-needed basis. In certain embodiments, such capabilities may includethe performance of operations associated with providing real-timeresolution of the identity of an entity at a particular point in time.In certain embodiments, such operations may include the provision ofassociated temporal information (e.g., time stamps).

In certain embodiments, the pluggable capabilities 212 may be sourcedfrom various cloud services 206. In certain embodiments, the pluggablehosting framework 208 may be implemented to provide certain computingand communication infrastructure components, and foundationcapabilities, required by one or more of the pluggable capabilities 212.In certain embodiments, the pluggable hosting framework 208 may beimplemented to allow the pluggable capabilities 212 to be dynamicallyinvoked. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 3 is a simplified block diagram of an endpoint agent implemented inaccordance with an embodiment of the invention. As used herein, anendpoint agent 306 broadly refers to a software agent used incombination with an endpoint device 304 to establish a protectedendpoint 302. Skilled practitioners of the art will be familiar withsoftware agents, which are computer programs that perform actions onbehalf of a user or another program. In various approaches, a softwareagent may be autonomous or work together with another agent or a user.In certain of these approaches the software agent is implemented toautonomously decide if a particular action is appropriate for a givenevent, such as an observed entity behavior, described in greater detailherein.

An endpoint device 304, as likewise used herein, refers to aninformation processing system such as a personal computer, a laptopcomputer, a tablet computer, a personal digital assistant (PDA), a smartphone, a mobile telephone, a digital camera, a video camera, or otherdevice that is capable of storing, processing and communicating data. Incertain embodiments, the communication of the data may take place inreal-time or near-real-time. As used herein, real-time broadly refers toprocessing and providing information within a time interval brief enoughto not be discernable by a user. As an example, a cellular phoneconversation may be used to communicate information in real-time, whilean instant message (IM) exchange may be used to communicate informationin near real-time. In certain embodiments, the communication of theinformation may take place asynchronously. For example, an email messagemay be stored on an endpoint device 304 when it is offline. In thisexample, the information may be communicated to its intended recipientonce the endpoint device 304 gains access to a network 140.

A protected endpoint 302, as likewise used herein, broadly refers to apolicy-based approach to network security that typically requiresendpoint devices 304 to comply with particular criteria before they aregranted access to network resources. As an example, a given endpointdevice 304 may be required to have a particular operating system (OS),or version thereof, a Virtual Private Network (VPN) client, anti-virussoftware with current updates, and so forth. In certain embodiments, theprotected endpoint 302 may be implemented to perform operationsassociated with providing real-time resolution of the identity of anentity at a particular point in time, as described in greater detailherein. In certain embodiments, the protected endpoint 302 may beimplemented to provide temporal information, such as timestampinformation, associated with such operations.

In certain embodiments, the real-time resolution of the identity of anentity at a particular point in time may be based upon contextualinformation associated with a given entity behavior. As used herein,contextual information broadly refers to any information, directly orindirectly, individually or in combination, related to a particularentity behavior. In certain embodiments, entity behavior may include anentity's physical behavior, cyber behavior, or a combination thereof. Aslikewise used herein, physical behavior broadly refers to any entitybehavior occurring within a physical realm. More particularly, physicalbehavior may include any action enacted by an entity that can beobjectively observed, or indirectly inferred, within a physical realm.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable user behavior broadly refers to any behaviorexhibited or enacted by a user that can be electronically observed.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or an entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system at a particular point in time. In this example,the individual actions performed by the user to download the data file,and associated temporal information, such as a time-stamp associatedwith the download, constitute a cyber behavior. In these examples, theactions are enacted within cyberspace, in combination with associatedtemporal information, makes them electronically-observable.

As likewise used herein, cyberspace broadly refers to a network 140environment capable of supporting communication between two or moreentities. In certain embodiments, the entity may be a user, an endpointdevice 304, or various resources, described in greater detail herein. Incertain embodiments, the entities may include various endpoint devices304 or resources operating at the behest of an entity, such as a user.In certain embodiments, the communication between the entities mayinclude audio, image, video, text, or binary data.

As described in greater detail herein, the contextual information mayinclude an entity's authentication factors. Contextual information maylikewise include various temporal identity resolution factors, such asidentification factors associated with the entity, thedate/time/frequency of various entity behaviors, the entity's location,the entity's role or position in an organization, their associatedaccess rights, and certain user gestures employed by the user in theenactment of a user behavior. Other contextual information may likewiseinclude various user interactions, whether the interactions are with anendpoint device 304, a network 140, a resource, or another user. Incertain embodiments, user behaviors, and their related contextualinformation, may be collected at particular points of observation, andat particular points in time, described in greater detail herein. Incertain embodiments, a protected endpoint 302 may be implemented as apoint of observation for the collection of entity behavior andcontextual information.

In certain embodiments, the endpoint agent 306 may be implemented touniversally support a variety of operating systems, such as AppleMacintosh®, Microsoft Windows®, Linux®, Android® and so forth. Incertain embodiments, the endpoint agent 306 may be implemented tointeract with the endpoint device 304 through the use of low-level hooks312 at the operating system level. It will be appreciated that the useof low-level hooks 312 allows the endpoint agent 306 to subscribe tomultiple events through a single hook. Consequently, multiplefunctionalities provided by the endpoint agent 306 can share a singledata stream, using only those portions of the data stream they mayindividually need. Accordingly, system efficiency can be improved andoperational overhead reduced.

In certain embodiments, the endpoint agent 306 may be implemented toprovide a common infrastructure for pluggable feature packs 308. Invarious embodiments, the pluggable feature packs 308 may provide certainsecurity management functionalities. Examples of such functionalitiesmay include various anti-virus and malware detection, data lossprotection (DLP), insider threat detection, and so forth. In certainembodiments, the security management functionalities may include one ormore functionalities associated with providing real-time resolution ofthe identity of an entity at a particular point in time, as described ingreater detail herein.

In certain embodiments, a particular pluggable feature pack 308 may beinvoked as needed by the endpoint agent 306 to provide a givenfunctionality. In certain embodiments, individual features of aparticular pluggable feature pack 308 are invoked as needed. It will beappreciated that the ability to invoke individual features of apluggable feature pack 308, without necessarily invoking all suchfeatures, will likely improve the operational efficiency of the endpointagent 306 while simultaneously reducing operational overhead.Accordingly, the endpoint agent 306 can self-optimize in certainembodiments by using the common infrastructure and invoking only thosepluggable components that are applicable or needed for a given userbehavior.

In certain embodiments, the individual features of a pluggable featurepack 308 are invoked by the endpoint agent 306 according to theoccurrence of a particular user behavior. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 according to the occurrence of a particular temporalevent, described in greater detail herein. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 at a particular point in time. In these embodiments,the method by which a given user behavior, temporal event, or point intime is selected is a matter of design choice.

In certain embodiments, the individual features of a pluggable featurepack 308 may be invoked by the endpoint agent 306 according to thecontext of a particular user behavior. As an example, the context may bethe user enacting the user behavior, their associated riskclassification, which resource they may be requesting, the point in timethe user behavior is enacted, and so forth. In certain embodiments, thepluggable feature packs 308 may be sourced from various cloud services206. In certain embodiments, the pluggable feature packs 308 may bedynamically sourced from various cloud services 206 by the endpointagent 306 on an as-needed basis.

In certain embodiments, the endpoint agent 306 may be implemented withadditional functionalities, such as event analytics 310. In certainembodiments, the event analytics 310 functionality may include analysisof various user behaviors, described in greater detail herein. Incertain embodiments, the endpoint agent 306 may be implemented with athin hypervisor 314, which can be run at Ring −1, thereby providingprotection for the endpoint agent 306 in the event of a breach. As usedherein, a thin hypervisor broadly refers to a simplified, OS-dependenthypervisor implemented to increase security. As likewise used herein,Ring −1 broadly refers to approaches allowing guest operating systems torun Ring 0 (i.e., kernel) operations without affecting other guests orthe host OS. Those of skill in the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 4 is a simplified block diagram of a security analytics systemimplemented in accordance with an embodiment of the invention. Incertain embodiments, the security analytics system 118 shown in FIG. 4may include an event queue analytics 404 module, described in greaterdetail herein. In certain embodiments, the event queue analytics 404sub-system may be implemented to include an enrichment 406 module and astreaming analytics 408 module. In certain embodiments, the securityanalytics system 118 may be implemented to provide log storage,reporting, and analytics capable of performing streaming 408 andon-demand 410 analytics operations. In certain embodiments, suchoperations may be associated with defining and managing an entitybehavior profile (EBP), described in greater detail herein. In certainembodiments, an EBP may be implemented as an adaptive trust profile(ATP). In certain embodiments, an EBP may be implemented to detectentity behavior that may be of analytic utility, adaptively respondingto mitigate risk, or a combination thereof, as described in greaterdetail herein. In certain embodiments, entity behavior of analyticutility may be determined to be anomalous, abnormal, unexpected,malicious, or some combination thereof, as likewise described in greaterdetail herein.

In certain embodiments, the security analytics system 118 may beimplemented to provide a uniform platform for storing events andcontextual information associated with various entity behaviors andperforming longitudinal analytics. As used herein, longitudinalanalytics broadly refers to performing analytics of entity behaviorsoccurring over a particular period of time. As an example, an entity mayiteratively attempt to access certain proprietary information stored invarious locations. In addition, the attempts may occur over a briefperiod of time. To continue the example, the fact that the informationthe user is attempting to access is proprietary, that it is stored invarious locations, and the attempts are occurring in a brief period oftime, in combination, may indicate the entity behavior enacted by theentity is suspicious. As another example, certain entity identifierinformation (e.g., a user name) associated with an entity may changeover time. In this example, a change in the entity's user name, during aparticular time period or at a particular point in time, may representsuspicious entity behavior.

In certain embodiments, the security analytics system 118 may beimplemented to be scalable. In certain embodiments, the securityanalytics system 118 may be implemented in a centralized location, suchas a corporate data center. In these embodiments, additional resourcesmay be added to the security analytics system 118 as needs grow. Incertain embodiments, the security analytics system 118 may beimplemented as a distributed system. In these embodiments, the securityanalytics system 118 may span multiple information handling systems. Incertain embodiments, the security analytics system 118 may beimplemented in a cloud environment. In certain embodiments, the securityanalytics system 118 may be implemented in a virtual machine (VM)environment. In such embodiments, the VM environment may be configuredto dynamically and seamlessly scale the security analytics system 118 asneeded. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

In certain embodiments, an event stream collector 402 may be implementedto collect event and related contextual information, described ingreater detail herein, associated with various entity behaviors. Inthese embodiments, the method by which the event and contextualinformation is selected to be collected by the event stream collector402 is a matter of design choice. In certain embodiments, the event andcontextual information collected by the event stream collector 402 maybe processed by an enrichment module 406 to generate enriched entitybehavior information. In certain embodiments, the enrichment may includecertain contextual information related to a particular entity behavioror event. In certain embodiments, the enrichment may include certaintemporal information, such as timestamp information, related to aparticular entity behavior or event.

In certain embodiments, enriched entity behavior information may beprovided by the enrichment module 406 to a streaming 408 analyticsmodule. In turn, the streaming 408 analytics module may provide some orall of the enriched entity behavior information to an on-demand 410analytics module. As used herein, streaming 408 analytics broadly refersto analytics performed in near real-time on enriched entity behaviorinformation as it is received. Likewise, on-demand 410 analytics broadlyrefers herein to analytics performed, as they are requested, on enrichedentity behavior information after it has been received. In certainembodiments, the enriched entity behavior information may be associatedwith a particular event. In certain embodiments, the enrichment 406 andstreaming analytics 408 modules may be implemented to perform eventqueue analytics 404 operations, as described in greater detail herein.

In certain embodiments, the on-demand 410 analytics may be performed onenriched entity behavior associated with a particular interval of, orpoint in, time. In certain embodiments, the streaming 408 or on-demand410 analytics may be performed on enriched entity behavior associatedwith a particular user, group of users, one or more non-user entities,or a combination thereof. In certain embodiments, the streaming 408 oron-demand 410 analytics may be performed on enriched entity behaviorassociated with a particular resource, such as a facility, system,datastore, or service. Those of skill in the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the results of various analytics operationsperformed by the streaming 408 or on-demand 410 analytics modules may beprovided to a storage Application Program Interface (API) 414. In turn,the storage API 412 may be implemented to provide access to variousdatastores ‘1’ 416 through ‘n’ 418, which in turn are used to store theresults of the analytics operations. In certain embodiments, thesecurity analytics system 118 may be implemented with a logging andreporting front-end 412, which is used to receive the results ofanalytics operations performed by the streaming 408 analytics module. Incertain embodiments, the datastores ‘1’ 416 through ‘n’ 418 mayvariously include a datastore of entity identifiers, temporal events, ora combination thereof.

In certain embodiments, the security analytics system 118 may include arisk scoring 420 module implemented to perform risk scoring operations,described in greater detail herein. In certain embodiments,functionalities of the risk scoring 420 module may be provided in theform of a risk management service 422. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with defining and managing an entity behavior profile (EBP),as described in greater detail herein. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with detecting entity behavior that may be of analyticutility and adaptively responding to mitigate risk, as described ingreater detail herein. In certain embodiments, the risk managementservice 422 may be implemented to provide the results of variousanalytics operations performed by the streaming 406 or on-demand 408analytics modules. In certain embodiments, the risk management service422 may be implemented to use the storage API 412 to access variousenhanced cyber behavior and analytics information stored on thedatastores ‘1’ 414 through ‘n’ 416. Skilled practitioners of the artwill recognize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 5 is a simplified block diagram of the operation of a securityanalytics system implemented in accordance with an embodiment of theinvention. In certain embodiments, the security analytics system 118 maybe implemented to perform operations associated with providing real-timeresolution of the identity of an entity at a particular point in time.In certain embodiments, the security analytics system 118 may beimplemented in combination with one or more endpoint agents 306, one ormore edge devices 202, cloud services 206, and a security analyticssystem 118, and a network 140 to perform such operations.

In certain embodiments, the network edge device 202 may be implementedin a bridge, a firewall, or a passive monitoring configuration. Incertain embodiments, the edge device 202 may be implemented as softwarerunning on an information processing system. In certain embodiments, thenetwork edge device 202 may be implemented to provide integratedlogging, updating and control. In certain embodiments, the edge device202 may be implemented to receive network requests and context-sensitivecyber behavior information in the form of enriched cyber behaviorinformation 510, described in greater detail herein, from an endpointagent 306, likewise described in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented as both a source and a sink of entity behavior information.In certain embodiments, the security analytics system 118 may beimplemented to serve requests for user/resource risk data. In certainembodiments, the edge device 202 and the endpoint agent 306,individually or in combination, may provide certain entity behaviorinformation to the security analytics system 118 using either push orpull approaches familiar to skilled practitioners of the art.

As described in greater detail herein, the edge device 202 may beimplemented in certain embodiments to receive enriched user behaviorinformation 510 from the endpoint agent 306. It will be appreciated thatsuch enriched user behavior information 510 will likely not be availablefor provision to the edge device 202 when an endpoint agent 306 is notimplemented for a corresponding endpoint device 304. However, the lackof such enriched user behavior information 510 may be accommodated invarious embodiments, albeit with reduced functionality associated withoperations associated with providing real-time resolution of theidentity of an entity at a particular point in time.

In certain embodiments, a given user behavior may be enriched by anassociated endpoint agent 306 attaching contextual information to arequest. In one embodiment, the context is embedded within a networkrequest, which is then provided as enriched user behavior information510. In another embodiment, the contextual information is concatenated,or appended, to a request, which in turn is provided as enriched userbehavior information 510. In these embodiments, the enriched userbehavior information 510 is unpacked upon receipt and parsed to separatethe request and its associated contextual information. Those of skill inthe art will recognize that one possible disadvantage of such anapproach is that it may perturb certain Intrusion Detection Systemand/or Intrusion Detection Prevention (IDS/IDP) systems implemented on anetwork 140.

In certain embodiments, new flow requests are accompanied by acontextual information packet sent to the edge device 202. In theseembodiments, the new flow requests may be provided as enriched userbehavior information 510. In certain embodiments, the endpoint agent 306may also send updated contextual information to the edge device 202 onceit becomes available. As an example, an endpoint agent 306 may share alist of files that have been read by a current process at any point intime once the information has been collected. To continue the example,such a list of files may be used to determine which data the endpointagent 306 may be attempting to exfiltrate.

In certain embodiments, point analytics processes executing on the edgedevice 202 may request a particular service. As an example, risk scoreson a per-user basis may be requested. In certain embodiments, theservice may be requested from the security analytics system 118. Incertain embodiments, the service may be requested from various cloudservices 206.

In certain embodiments, contextual information associated with a userbehavior may be attached to various network service requests. In certainembodiments, the request may be wrapped and then handled by proxy. Incertain embodiments, a small packet of contextual information associatedwith a user behavior may be sent with a service request. In certainembodiments, service requests may be related to Domain Name Service(DNS), web, email, and so forth, all of which are essentially requestsfor service by an endpoint device 304. In certain embodiments, suchservice requests may be associated with temporal event information,described in greater detail herein. Consequently, such requests can beenriched by the addition of user behavior contextual information (e.g.,UserAccount, interactive/automated, data-touched, temporal eventinformation, etc.). Accordingly, the edge device 202 can then use thisinformation to manage the appropriate response to submitted requests. Incertain embodiments, such requests may be associated with providingreal-time resolution of the identity of an entity at a particular pointin time.

In certain embodiments, the security analytics system 118 may beimplemented in different operational configurations. In one embodiment,the security analytics system 118 may be implemented by using theendpoint agent 306. In another embodiment, the security analytics system118 may be implemented by using endpoint agent 306 in combination withthe edge device 202. In certain embodiments, the cloud services 206 maylikewise be implemented for use by the endpoint agent 306, the edgedevice 202, and the security analytics system 118, individually or incombination. In these embodiments, the security analytics system 118 maybe primarily oriented to performing risk assessment operations relatedto user actions, program actions, data accesses, or a combinationthereof. In certain embodiments, program actions may be treated as aproxy for the user.

In certain embodiments, the endpoint agent 306 may be implemented toupdate the security analytics system 118 with user behavior andassociated contextual information, thereby allowing an offload ofcertain analytics processing overhead. In one embodiment, this approachallows for longitudinal risk scoring, which assesses risk associatedwith certain user behavior during a particular interval of time. Inanother embodiment, the security analytics system 118 may be implementedto perform risk-adaptive operations to access risk scores associatedwith the same user account, but accrued on different endpoint devices304. It will be appreciated that such an approach may prove advantageouswhen an adversary is “moving sideways” through a network environment,using different endpoint devices 304 to collect information.

In certain embodiments, the security analytics system 118 may beprimarily oriented to applying risk mitigations in a way that maximizessecurity effort return-on-investment (ROI). In certain embodiments, theapproach may be accomplished by providing additional contextual and userbehavior information associated with user requests. As an example, a webgateway may not concern itself with why a particular file is beingrequested by a certain entity at a particular point in time.Accordingly, if the file cannot be identified as malicious or harmless,there is no context available to determine how, or if, to proceed.

To extend the example, the edge device 202 and security analytics system118 may be coupled such that requests can be contextualized and fittedinto a framework that evaluates their associated risk. It will beappreciated that such an embodiment works well with web-based data lossprotection (DLP) approaches, as each transfer is no longer examined inisolation, but in the broader context of an identified user's actions,at a particular time, on the network 140.

As another example, the security analytics system 118 may be implementedto perform risk scoring processes to decide whether to block or allowunusual flows. It will be appreciated that such an approach is highlyapplicable to defending against point-of-sale (POS) malware, a breachtechnique that has become increasingly more common in recent years. Itwill likewise be appreciated that while various edge device 202implementations may not stop all such exfiltrations, they may be able tocomplicate the task for the attacker.

In certain embodiments, the security analytics system 118 may beprimarily oriented to maximally leverage contextual informationassociated with various user behaviors within the system. In certainembodiments, data flow tracking is performed by one or more endpointagents 306, which allows the quantity and type of information associatedwith particular hosts to be measured. In turn, this information may beused to determine how the edge device 202 handles requests. Bycontextualizing such user behavior on the network 140, the securityanalytics system 118 can provide intelligent protection, makingdecisions that make sense in the broader context of an organization'sactivities. It will be appreciated that one advantage to such anapproach is that information flowing through an organization, and thenetworks they employ, should be trackable, and substantial data breachespreventable. Skilled practitioners of the art will recognize that manysuch embodiments and examples are possible. Accordingly, the foregoingis not intended to limit the spirit, scope or intent of the invention.

FIG. 6 shows a simplified block diagram of an entity behavior profile(EBP) implemented in accordance with an embodiment of the invention. Asused herein, an entity behavior profile 638 broadly refers to acollection of information that uniquely describes a particular entity'sidentity and their associated behavior, whether the behavior occurswithin a physical realm or cyberspace. In certain embodiments, an EBP638 may be used to adaptively draw inferences regarding thetrustworthiness of a particular entity. In certain embodiments, asdescribed in greater detail herein, the drawing of the inferences mayinvolve comparing a new entity behavior to known past behaviors enactedby the entity. In certain embodiments, new entity behavior of analyticutility may represent entity behavior that represents a security risk.As likewise used herein, an entity broadly refers to something thatexists as itself, whether physically or abstractly. In certainembodiments, an entity may be a user entity, a non-user entity, or acombination thereof. In certain embodiments, the identity of an entitymay be known or unknown.

As used herein, a user entity broadly refers to an entity capable ofenacting a user entity behavior, as described in greater detail herein.Examples of a user entity include an individual person, a group ofpeople, an organization, or a government. As likewise used herein, anon-user entity broadly refers to an entity whose identity can bedescribed and may exhibit certain behavior, but is incapable of enactinga user entity behavior. Examples of a non-user entity include an item, adevice, such as endpoint and edge devices, a network, an account, adomain, an operation, a process, and an event. Other examples of anon-user entity include a resource, such as a geographical location orformation, a physical facility, a venue, a system, a softwareapplication, a data store, and a service, such as a service operating ina cloud environment.

Certain embodiments of the invention reflect an appreciation that beingable to uniquely identity a device may assist in establishing whether ornot a particular login is legitimate. As an example, user impersonationsmay not occur at the user's endpoint, but instead, from another deviceor system. Certain embodiments of the invention likewise reflect anappreciation that profiling the entity behavior of a particular deviceor system may assist in determining whether or not it is actingsuspiciously.

In certain embodiments, an account may be local account, which runs on asingle machine. In certain embodiments, an account may be a globalaccount, providing access to multiple resources. In certain embodiments,a process may be implemented to run in an unattended mode, such as whenbacking up files or checking for software updates. Certain embodimentsof the invention reflect an appreciation that it is often advantageousto track events at the process level as a method of determining whichevents are associated with background processes and which are initiatedby a user entity.

In certain embodiments, an EBP 638 may be implemented to include a userentity profile 602, an associated user entity mindset profile 630, anon-user entity profile 632, and an entity state 636. As used herein, auser entity profile 602 broadly refers to a collection of informationthat uniquely describes a user entity's identity and their associatedbehavior, whether the behavior occurs within a physical realm orcyberspace. In certain embodiments, as described in greater detailherein, the user entity profile 602 may include user profile attributes604, user behavior factors 610, user mindset factors 622, or acombination thereof. In certain embodiments, the user profile attributes604 may include certain user authentication factors 606, described ingreater detail herein, and personal information 608.

As used herein, a user profile attribute 604 broadly refers to data ormetadata that can be used, individually or in combination with otheruser profile attributes 604, user behavior factors 610, or user mindsetfactors 622, to ascertain the identity of a user entity. In variousembodiments, certain user profile attributes 604 may be uniquelyassociated with a particular user entity. In certain embodiments, thepersonal information 608 may include non-sensitive personal informationassociated with a user entity, such as their name, title, position,role, and responsibilities. In certain embodiments, the personalinformation 608 may likewise include technical skill level information,peer information, expense account information, paid time off (PTO)information, data analysis information, insider information,misconfiguration information, third party information, or a combinationthereof. In certain embodiments, the personal information 608 maycontain sensitive personal information associated with a user entity. Asused herein, sensitive personal information (SPI), also commonlyreferred to as personally identifiable information (PII), broadly refersto any information usable to ascertain the identity of a user entity,either by itself, or in combination with other information, such ascontextual information described in greater detail herein.

Examples of SPI may include the full or legal name of a user entity,initials or nicknames, place and date of birth, home and businessaddresses, personal and business telephone numbers, their gender, andother genetic information. Additional examples of SPI may includegovernment-issued identifiers, such as a Social Security Number (SSN) ora passport number, vehicle registration plate and serial numbers, anddriver's license numbers. Other examples of SPI may include certainemail addresses and social media identifiers, credit and debit cardnumbers, and other digital identity information. Yet other examples ofSPI may include employer-issued identifiers, financial transactioninformation, credit scores, electronic medical records (EMRs), insuranceclaim information, personal correspondence, and so forth. Furtherexamples of SPI may include user authentication factors 606, such asbiometrics, user identifiers and passwords, and personal identificationnumbers (PINs).

In certain embodiments, the SPI may include information considered by anindividual user, a group of users, or an organization (e.g., a company,a government or non-government organization, etc.), to be confidentialor proprietary. One example of such confidential information isprotected health information (PHI). As used herein, PHI broadly refersto any information associated with the health status, provision ofhealth care, or payment for health care that is created or collected bya “covered entity,” or an associate thereof, that can be linked to aparticular individual. As used herein, a “covered entity” broadly refersto health plans, healthcare clearinghouses, healthcare providers, andothers, who may electronically communicate any health-relatedinformation associated with a particular individual. Examples of suchPHI may include any part of a patient's medical record, healthcarerecord, or payment history for medical or healthcare services.

As used herein, a user behavior factor 610 broadly refers to informationassociated with a user entity's behavior, whether the behavior occurswithin a physical realm or cyberspace. In certain embodiments, userbehavior factors 610 may include the user entity's access rights 612,the user entity's interactions 614, and the date/time/frequency 616 ofwhen the interactions 614 are enacted. In certain embodiments, the userbehavior factors 610 may likewise include the user entity's location618, and the gestures 620 used by the user entity to enact theinteractions 614.

In certain embodiments, the user entity gestures 620 may include keystrokes on a keypad, a cursor movement, a mouse movement or click, afinger swipe, tap, or other hand gesture, an eye movement, or somecombination thereof. In certain embodiments, the user entity gestures620 may likewise include the cadence of the user's keystrokes, themotion, force and duration of a hand or finger gesture, the rapidity anddirection of various eye movements, or some combination thereof. Incertain embodiments, the user entity gestures 620 may include variousaudio or verbal commands performed by the user.

As used herein, user mindset factors 622 broadly refer to informationused to make inferences regarding the mental state of a user entity at aparticular point in time, during the occurrence of an event or anenactment of a user behavior, or a combination thereof. As likewise usedherein, mental state broadly refers to a hypothetical statecorresponding to the way a user entity may be thinking or feeling.Likewise, as used herein, an event broadly refers to the occurrence ofan action performed by an entity. In certain embodiments, the userentity mindset factors 622 may include a personality type 624. Examplesof known approaches for determining a personality type 624 includeJungian types, Myers-Briggs type indicators, Keirsey Temperament Sorter,Socionics, Enneagram of Personality, and Eyseneck's three-factor model.

In certain embodiments, the user mindset factors 622 may include variousbehavioral biometrics 626. As used herein, a behavioral biometric 628broadly refers to a physiological indication of a user entity's mentalstate. Examples of behavioral biometrics 626 may include a user entity'sblood pressure, heart rate, respiratory rate, eye movements and irisdilation, facial expressions, body language, tone and pitch of voice,speech patterns, and so forth.

Certain embodiments of the invention reflect an appreciation thatcertain user behavior factors 610, such as user entity gestures 620, mayprovide additional information related to inferring a user entity'smental state. As an example, a user entering text at a quick pace with arhythmic cadence may indicate intense focus. Likewise, an individualuser intermittently entering text with forceful keystrokes may indicatethe user is in an agitated state. As another example, the user mayintermittently enter text somewhat languorously, which may indicatebeing in a thoughtful or reflective state of mind. As yet anotherexample, the user may enter text with a light touch with an unevencadence, which may indicate the user is hesitant or unsure of what isbeing entered.

Certain embodiments of the invention likewise reflect an appreciationthat while the user entity gestures 620 may provide certain indicationsof the mental state of a particular user entity, they may not providethe reason for the user entity to be in a particular mental state.Likewise, certain embodiments of the invention include an appreciationthat certain user entity gestures 620 and behavioral biometrics 626 arereflective of an individual user's personality type 624. As an example,aggressive, forceful keystrokes combined with an increased heart ratemay indicate normal behavior for a particular user when composingend-of-month performance reviews. In various embodiments, certain userentity behavior factors 610, such as user gestures 620, may becorrelated with certain contextual information, as described in greaterdetail herein.

In certain embodiments, a security analytics system 118, described ingreater detail herein, may be implemented to include an entity behaviorcatalog (EBC) system 120. In certain embodiments, the EBC system 120 maybe implemented to generate, manage, store, or some combination thereof,information related to the behavior of an associated entity. In variousembodiments, the EBC system 120 may be implemented as a cyber behaviorcatalog. In certain of these embodiments, the cyber behavior catalog maybe implemented to generate, manage, store, or some combination thereof,information related to cyber behavior, described in greater detailherein, enacted by an associated entity. In various embodiments, aslikewise described in greater detail herein, the information generated,managed, stored, or some combination thereof, by such a cyber behaviorcatalog, may be related to cyber behavior enacted by a user entity, anon-user entity, or a combination thereof.

In certain embodiments, the EBC system 120 may be implemented to use auser entity profile 602 in combination with an entity state 636 togenerate a user entity mindset profile 630. As used herein, entity state636 broadly refers to the context of a particular event or entitybehavior. In certain embodiments, the entity state 636 may be along-term entity state or a short-term entity state. As used herein, along-term entity state 636 broadly relates to an entity state 636 thatpersists for an extended interval of time, such as six months or a year.As likewise used herein, a short-term entity state 636 broadly relatesto an entity state 636 that occurs for a brief interval of time, such asa few minutes or a day. In various embodiments, the method by which anentity state's 636 associated interval of time is considered to belong-term or short-term is a matter of design choice.

As an example, a particular user may have a primary work location, suchas a branch office, and a secondary work location, such as theircompany's corporate office. In this example, the user's primary andsecondary offices respectively correspond to the user's location 618,whereas the presence of the user at either office corresponds to anentity state 636. To continue the example, the user may consistentlywork at their primary office Monday through Thursday, but at theircompany's corporate office on Fridays. To further continue the example,the user's presence at their primary work location may be a long-termentity state 636, while their presence at their secondary work locationmay be a short-term entity state 636. Accordingly, a date/time/frequency616 user entity behavior factor 610 can likewise be associated with userbehavior respectively enacted on those days, regardless of theircorresponding locations. Consequently, the long-term user entity state636 on Monday through Thursday will typically be “working at the branchoffice” and the short-term entity state 636 on Friday will likely be“working at the corporate office.”

As likewise used herein, a user entity mindset profile 630 broadlyrefers to a collection of information that reflects an inferred mentalstate of a user entity at a particular time during the occurrence of anevent or an enactment of a user behavior. As an example, certaininformation may be known about a user entity, such as their name, theirtitle and position, and so forth, all of which are user profileattributes 604. Likewise, it may be possible to observe a user entity'sassociated user behavior factors 610, such as their interactions withvarious systems, when they log-in and log-out, when they are active atthe keyboard, the rhythm of their keystrokes, and which files theytypically use.

Certain embodiments of the invention reflect an appreciation thesebehavior factors 610 can be considered to be a behavioral fingerprint.In certain embodiments, the user behavior factors 610 may change, alittle or a lot, from day to day. These changes may be benign, such aswhen a user entity begins a new project and accesses new data, or theymay indicate something more concerning, such as a user entity who isactively preparing to steal data from their employer. In certainembodiments, the user behavior factors 610 may be implemented toascertain the identity of a user entity. In certain embodiments, theuser behavior factors 610 may be uniquely associated with a particularentity.

In certain embodiments, observed user behaviors may be used to build auser entity profile 602 for a particular user or other entity. Inaddition to creating a model of a user's various attributes and observedbehaviors, these observations can likewise be used to infer things thatare not necessarily explicit. Accordingly, in certain embodiments, abehavioral fingerprint may be used in combination with an EBP 638 togenerate an inference regarding an associated user entity. As anexample, a particular user may be observed eating a meal, which may ormay not indicate the user is hungry. However, if it is also known thatthe user worked at their desk throughout lunchtime and is now eating asnack during a mid-afternoon break, then it can be inferred they areindeed hungry.

As likewise used herein, a non-user entity profile 632 broadly refers toa collection of information that uniquely describes a non-user entity'sidentity and their associated behavior, whether the behavior occurswithin a physical realm or cyberspace. In various embodiments, thenon-user entity profile 632 may be implemented to include certainnon-user profile attributes 634. As used herein, a non-user profileattribute 634 broadly refers to data or metadata that can be used,individually or in combination with other non-user profile attributes634, to ascertain the identity of a non-user entity. In variousembodiments, certain non-user profile attributes 634 may be uniquelyassociated with a particular non-user entity.

In certain embodiments, the non-user profile attributes 634 may beimplemented to include certain identity information, such as a non-userentity's network, Media Access Control (MAC), or physical address, itsserial number, associated configuration information, and so forth. Invarious embodiments, the non-user profile attributes 634 may beimplemented to include non-user behavior information associated withinteractions between certain user and non-user entities, the type ofthose interactions, the data exchanged during the interactions, thedate/time/frequency of such interactions, and certain services accessedor provided.

In various embodiments, the EBC system 120 may be implemented to usecertain data associated with an EBP 638 to provide a probabilisticmeasure of whether a particular electronically-observable event is ofanalytic utility. In certain embodiments, an electronically-observableevent that is of analytic utility may be determined to be anomalous,abnormal, unexpected, or malicious. To continue the prior example, auser may typically work out of their company's corporate office onFridays. Furthermore, various user mindset factors 622 within theirassociated user entity profile 602 may indicate that the user istypically relaxed and methodical when working with customer data.Moreover, the user's user entity profile 602 indicates that such userinteractions 614 with customer data typically occur on Monday morningsand the user rarely, if ever, copies or downloads customer data.However, the user may decide to interact with certain customer data lateat night, on a Friday, while in their company's corporate office. Asthey do so, they exhibit an increased heart rate, rapid breathing, andfurtive keystrokes while downloading a subset of customer data to aflash drive.

Consequently, their user entity mindset profile 630 may reflect anervous, fearful, or guilty mindset, which is inconsistent with theentity state 634 of dealing with customer data in general. Moreparticularly, downloading customer data late at night on a day the useris generally not in their primary office results in an entity state 634that is likewise inconsistent with the user's typical user behavior. Asa result, the EBC system 120 may infer that the user's behavior mayrepresent a security threat. Those of skill in the art will recognizethat many such embodiments and examples are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

Certain embodiments of the invention reflect an appreciation that thequantity, and relevancy, of information contained in a particular EBP638 may have a direct bearing on its analytic utility when attempting todetermine the trustworthiness of an associated entity and whether or notthey represent a security risk. As used herein, the quantity ofinformation contained in a particular EBP 638 broadly refers to thevariety and volume of EBP elements it may contain, and the frequency oftheir respective instances, or occurrences, related to certain aspectsof an associated entity's identity and behavior. As used herein, an EBPelement broadly refers to any data element stored in an EBP 638, asdescribed in greater detail herein. In various embodiments, an EBPelement may be used to describe a particular aspect of an EBP, such ascertain user profile attributes 604, user behavior factors 610, usermindset factors 622, user entity mindset profile 630, non-user profileattributes 634, and entity state 636.

In certain embodiments, statistical analysis may be performed on theinformation contained in a particular EBP 638 to determine thetrustworthiness of its associated entity and whether or not theyrepresent a security risk. For example, a particular authenticationfactor 606, such as a biometric, may be consistently used by a userentity for authenticating their identity to their endpoint device. Tocontinue the example, a user ID and password may be used by the same, ora different user entity, in an attempt to access the endpoint device. Asa result, the use of a user ID and password may indicate a security riskdue to its statistical infrequency. As another example, a user entitymay consistently access three different systems on a daily basis intheir role as a procurement agent. In this example, the three systemsmay include a financial accounting system, a procurement system, and aninventory control system. To continue the example, an attempt by theprocurement agent to access a sales forecast system may appearsuspicious if never attempted before, even if the purpose for accessingthe system is legitimate.

As likewise used herein, the relevancy of information contained in aparticular EBP 638 broadly refers to the pertinence of the EBP elementsit may contain to certain aspects of an associated entity's identity andbehavior. To continue the prior example, an EBP 638 associated with theprocurement agent may contain certain user profile attributes 604related to their title, position, role, and responsibilities, all orwhich may be pertinent to whether or not they have a legitimate need toaccess the sales forecast system. In certain embodiments, the userprofile attributes 604 may be implemented to include certain jobdescription information. To further continue the example, such jobdescription information may have relevance when attempting to determinewhether or not the associated entity's behavior is suspicious. Infurther continuance of the example, job description information relatedto the procurement agent may include their responsibility to check salesforecast data, as needed, to ascertain whether or not to procure certainitems. In these embodiments, the method by which it is determinedwhether the information contained in a particular EBP 638 is ofsufficient quantity and relevancy is a matter of design choice.

Various embodiments of the invention likewise reflect an appreciationthat accumulating sufficient information in an EBP 638 to make such adetermination may take a certain amount of time. Likewise, variousembodiments of the invention reflect an appreciation that theeffectiveness or accuracy of such a determination may rely upon certainentity behaviors occurring with sufficient frequency, or in identifiablepatterns, or a combination thereof, during a particular period of time.As an example, there may not be sufficient occurrences of a particulartype of entity behavior to determine if a new entity behavior isinconsistent with known past occurrences of the same type of entitybehavior. Accordingly, various embodiments of the invention reflect anappreciation that a sparsely-populated EBP 638 may likewise result inexposure to certain security vulnerabilities. Furthermore, the relevanceof such sparsely-populated information initially contained in an EBP 638first implemented may not prove very useful when using an EBP 638 todetermine the trustworthiness of an associated entity and whether or notthey represent a security risk.

FIGS. 7a and 7b show a block diagram of a security analytics environmentimplemented in accordance with an embodiment of the invention. Incertain embodiments, a security analytics system 118 may be implementedwith an entity behavior catalog (EBC) system 120, a human-centric riskmodeling system 122, or both. In certain embodiments, analyses performedby the security analytics system 118 may be used to identify behaviorassociated with a particular entity that may be of analytic utility.

In certain embodiments, as likewise described in greater detail herein,the EBC system 120, or the human-centric risk modeling system 122, orboth, may be used in combination with the security analytics system 118to perform such analyses. In various embodiments, certain data stored ina repository of security analytics data 680, or a repository of EBC data690, or both, may be used by the security analytics system 118, the EBCsystem 120, the human-centric risk modeling system 122, or somecombination thereof, to perform the analyses. Likewise, certain datastored in a repository of risk modeling data 770 may be used by thesecurity analytics system 118, the EBC system 120, or the human-centricrisk modeling system 122, or a combination thereof, to perform theanalyses.

In certain embodiments, the entity behavior of analytic utility may beidentified at a particular point in time, during the occurrence of anevent, the enactment of a user or non-user entity behavior, or acombination thereof. As used herein, an entity broadly refers tosomething that exists as itself, whether physically or abstractly. Incertain embodiments, an entity may be a user entity, a non-user entity,or a combination thereof. In certain embodiments, a user entity may bean individual user, such as user ‘A’ 702 or ‘B’ 772, a group, anorganization, or a government. In certain embodiments, a non-user entitymay likewise be an item, a device, such as endpoint 304 and edge 202devices, a network, such as an internal 744 and external 746 networks, adomain, an operation, or a process. In certain embodiments, a non-userentity may be a resource 750, such as a geographical location orformation, a physical facility 752, such as a venue, various physicalsecurity devices 754, a system 756, shared devices 758, such as printer,scanner, or copier, a data store 760, or a service 762, such as aservice 762 operating in a cloud environment.

As likewise used herein, an event broadly refers to the occurrence of anaction performed by an entity. In certain embodiments, the action may bedirectly associated with an entity behavior, described in greater detailherein. As an example, a first user may attach a binary file infectedwith a virus to an email that is subsequently sent to a second user. Inthis example, the act of attaching the binary file to the email isdirectly associated with an entity behavior enacted by the first user.In certain embodiments, the action may be indirectly associated with anentity behavior. To continue the example, the recipient of the email mayopen the infected binary file, and as a result, infect their computerwith malware. To further continue the example, the act of opening theinfected binary file is directly associated with an entity behaviorenacted by the second user. However, the infection of the emailrecipient's computer by the infected binary file is indirectlyassociated with the described entity behavior enacted by the seconduser.

In various embodiments, certain user authentication factors 606 may beused to authenticate the identity of a user entity. In certainembodiments, the user authentication factors 606 may be used to ensurethat a particular user entity, such as user ‘A’ 702 or ‘B’ 772, isassociated with their corresponding user entity profile 602, rather thana user entity profile 602 associated with another user. In certainembodiments, the user authentication factors 606 may include a user'sbiometrics 706 (e.g., a fingerprint or retinal scan), tokens 708 (e.g.,a dongle containing cryptographic keys), user identifiers and passwords(ID/PW) 710, and personal identification numbers (PINs).

In certain embodiments, information associated with such user entitybehavior may be stored in a user entity profile 602, described ingreater detail herein. In certain embodiments, the user entity profile602 may be stored in a repository of entity behavior catalog (EBC) data690. In certain embodiments, as likewise described in greater detailherein, the user entity profile 602 may include user profile attributes604, user behavior factors 610, user mindset factors 622, or acombination thereof. As used herein, a user profile attribute 604broadly refers to data or metadata that can be used, individually or incombination with other user profile attributes 604, user behaviorfactors 610, or user mindset factors 622, to ascertain the identity of auser entity. In various embodiments, certain user profile attributes 604may be uniquely associated with a particular user entity.

As likewise used herein, a user behavior factor 610 broadly refers toinformation associated with a user's behavior, whether the behavioroccurs within a physical realm or cyberspace. In certain embodiments,the user behavior factors 610 may include the user's access rights 612,the user's interactions 614, and the date/time/frequency 616 of thoseinteractions 614. In certain embodiments, the user behavior factors 610may likewise include the user's location 618 when the interactions 614are enacted, and the user gestures 620 used to enact the interactions614.

In various embodiments, certain date/time/frequency 616 user behaviorfactors 610 may be implemented as ontological or societal time, or acombination thereof. As used herein, ontological time broadly refers tohow one instant in time relates to another in a chronological sense. Asan example, a first user behavior enacted at 12:00 noon on May 17, 2017may occur prior to a second user behavior enacted at 6:39 PM on May 18,2018. Skilled practitioners of the art will recognize one value ofontological time is to determine the order in which various userbehaviors have been enacted.

As likewise used herein, societal time broadly refers to the correlationof certain user profile attributes 604, user behavior factors 610, usermindset factors 622, or a combination thereof, to one or more instantsin time. As an example, user ‘A’ 702 may access a particular system 756to download a customer list at 3:47 PM on Nov. 3, 2017. Analysis oftheir user behavior profile indicates that it is not unusual for user‘A’ 702 to download the customer list on a weekly basis. However,examination of their user behavior profile also indicates that user ‘A’702 forwarded the downloaded customer list in an email message to user‘B’ 772 at 3:49 PM that same day. Furthermore, there is no record intheir user behavior profile that user ‘A’ 702 has ever communicated withuser B′ 772 in the past. Moreover, it may be determined that user ‘B’872 is employed by a competitor. Accordingly, the correlation of user‘A’ 702 downloading the customer list at one point in time, and thenforwarding the customer list to user ‘B’ 772 at a second point in timeshortly thereafter, is an example of societal time.

In a variation of the prior example, user ‘A’ 702 may download thecustomer list at 3:47 PM on Nov. 3, 2017. However, instead ofimmediately forwarding the customer list to user ‘B’ 772, user ‘A’ 702leaves for a two week vacation. Upon their return, they forward thepreviously-downloaded customer list to user ‘B’ 772 at 9:14 AM on Nov.20, 2017. From an ontological time perspective, it has been two weekssince user ‘A’ 702 accessed the system 756 to download the customerlist. However, from a societal time perspective, they have stillforwarded the customer list to user ‘B’ 772, despite two weeks havingelapsed since the customer list was originally downloaded.

Accordingly, the correlation of user ‘A’ 702 downloading the customerlist at one point in time, and then forwarding the customer list to user‘B’ 772 at a much later point in time, is another example of societaltime. More particularly, it may be inferred that the intent of user ‘A’702 did not change during the two weeks they were on vacation.Furthermore, user ‘A’ 702 may have attempted to mask an intendedmalicious act by letting some period of time elapse between the timethey originally downloaded the customer list and when they eventuallyforwarded it to user ‘B’ 772. From the foregoing, those of skill in theart will recognize that the use of societal time may be advantageous indetermining whether a particular entity behavior is of analytic utility.As used herein, mindset factors 622 broadly refer to information used toinfer the mental state of a user at a particular point in time, duringthe occurrence of an event, an enactment of a user behavior, orcombination thereof.

In certain embodiments, the security analytics system 118 may beimplemented to process certain entity attribute information, describedin greater detail herein, associated with providing resolution of theidentity of an entity at a particular point in time. In variousembodiments, the security analytics system 118 may be implemented to usecertain entity identifier information, likewise described in greaterdetail herein, to ascertain the identity of an associated entity at aparticular point in time. In various embodiments, the entity identifierinformation may include certain temporal information, described ingreater detail herein. In certain embodiments, the temporal informationmay be associated with an event associated with a particular point intime.

In certain embodiments, the security analytics system 118 may beimplemented to use information associated with certain entity behaviorelements to resolve the identity of an entity at a particular point intime. An entity behavior element, as used herein, broadly refers to adiscrete element of an entity's behavior during the performance of aparticular operation in a physical realm, cyberspace, or a combinationthereof. In certain embodiments, such entity behavior elements may beassociated with a user/device 730, a user/network 742, a user/resource748, a user/user 770 interaction, or a combination thereof.

As an example, user ‘A’ 702 may use an endpoint device 304 to browse aparticular web page on a news site on an external system 776. In thisexample, the individual actions performed by user ‘A’ 702 to access theweb page are entity behavior elements that constitute an entitybehavior, described in greater detail herein. As another example, user‘A’ 702 may use an endpoint device 304 to download a data file from aparticular system 756. In this example, the individual actions performedby user ‘A’ 702 to download the data file, including the use of one ormore user authentication factors 606 for user authentication, are entitybehavior elements that constitute an entity behavior. In certainembodiments, the user/device 730 interactions may include an interactionbetween a user, such as user ‘A’ 702 or ‘B’ 772, and an endpoint device304.

In certain embodiments, the user/device 730 interaction may includeinteraction with an endpoint device 304 that is not connected to anetwork at the time the interaction occurs. As an example, user ‘A’ 702or ‘B’ 772 may interact with an endpoint device 304 that is offline,using applications 732, accessing data 734, or a combination thereof, itmay contain. Those user/device 730 interactions, or their result, may bestored on the endpoint device 304 and then be accessed or retrieved at alater time once the endpoint device 304 is connected to the internal 744or external 746 networks. In certain embodiments, an endpoint agent 306may be implemented to store the user/device 730 interactions when theuser device 304 is offline.

In certain embodiments, an endpoint device 304 may be implemented with adevice camera 728. In certain embodiments, the device camera 728 may beintegrated into the endpoint device 304. In certain embodiments, thedevice camera 728 may be implemented as a separate device configured tointeroperate with the endpoint device 304. As an example, a webcamfamiliar to those of skill in the art may be implemented receive andcommunicate various image and audio signals to an endpoint device 304via a Universal Serial Bus (USB) interface.

In certain embodiments, the device camera 728 may be implemented tocapture and provide user/device 730 interaction information to anendpoint agent 306. In various embodiments, the device camera 728 may beimplemented to provide surveillance information related to certainuser/device 730 or user/user 770 interactions. In certain embodiments,the surveillance information may be used by the security analyticssystem 118 to detect entity behavior associated with a user entity, suchas user ‘A’ 702 or user ‘B’ 772 that may be of analytic utility.

In certain embodiments, the endpoint device 304 may be used tocommunicate data through the use of an internal network 744, an externalnetwork 746, or a combination thereof. In certain embodiments, theinternal 744 and the external 746 networks may include a public network,such as the Internet, a physical private network, a virtual privatenetwork (VPN), or any combination thereof. In certain embodiments, theinternal 744 and external 746 networks may likewise include a wirelessnetwork, including a personal area network (PAN), based on technologiessuch as Bluetooth. In various embodiments, the wireless network mayinclude a wireless local area network (WLAN), based on variations of theIEEE 802.11 specification, commonly referred to as WiFi. In certainembodiments, the wireless network may include a wireless wide areanetwork (WWAN) based on an industry standard including various 3G, 4Gand 5G technologies.

In certain embodiments, the user/user 770 interactions may includeinteractions between two or more user entities, such as user ‘A’ 702 and‘B’ 772. In certain embodiments, the user/user interactions 770 may bephysical, such as a face-to-face meeting, via a user/device 730interaction, a user/network 742 interaction, a user/resource 748interaction, or some combination thereof. In certain embodiments, theuser/user 770 interaction may include a face-to-face verbal exchange. Incertain embodiments, the user/user 770 interaction may include a writtenexchange, such as text written on a sheet of paper. In certainembodiments, the user/user 770 interaction may include a face-to-faceexchange of gestures, such as a sign language exchange.

In certain embodiments, temporal event information associated withvarious user/device 730, user/network 742, user/resource 748, oruser/user 770 interactions may be collected and used to providereal-time resolution of the identity of an entity at a particular pointin time. Those of skill in the art will recognize that many suchexamples of user/device 730, user/network 742, user/resource 748, anduser/user 770 interactions are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, the security analytics system 118 may beimplemented to process certain contextual information in the performanceof certain security analytic operations. As used herein, contextualinformation broadly refers to any information, directly or indirectly,individually or in combination, related to a particular entity behavior.In certain embodiments, entity behavior may include a user entity'sphysical behavior, cyber behavior, or a combination thereof. As likewiseused herein, a user entity's physical behavior broadly refers to anyuser behavior occurring within a physical realm, such as speaking,gesturing, facial patterns or expressions, walking, and so forth. Moreparticularly, such physical behavior may include any action enacted byan entity user that can be objectively observed, or indirectly inferred,within a physical realm. In certain embodiments, the objectiveobservation, or indirect inference, of the physical behavior may beperformed electronically.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable user behavior broadly refers to any behaviorexhibited or enacted by a user entity that can be observed through theuse of an electronic device (e.g., an electronic sensor), a computingdevice or system (e.g., an endpoint 304 or edge 202 device, a physicalsecurity device 754, a system 756, a shared device 758, etc.), computerinstructions (e.g., a software application), or a combination thereof.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or other entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system 756 at a particular point in time. In thisexample, the individual actions performed by the user to download thedata file, and associated temporal information, such as a time-stampassociated with the download, constitute a cyber behavior. In theseexamples, the actions are enacted within cyberspace, in combination withassociated temporal information, which makes themelectronically-observable.

In certain embodiments, the contextual information may include locationdata 736. In certain embodiments, the endpoint device 304 may beconfigured to receive such location data 736, which is used as a datasource for determining the user's location 618. In certain embodiments,the location data 736 may include Global Positioning System (GPS) dataprovided by a GPS satellite 738. In certain embodiments, the locationdata 736 may include location data 736 provided by a wireless network,such as from a cellular network tower 740. In certain embodiments (notshown), the location data 736 may include various Internet Protocol (IP)or other network address information assigned to the endpoint 304 oredge 202 device. In certain embodiments (also not shown), the locationdata 736 may include recognizable structures or physical addresseswithin a digital image or video recording.

In certain embodiments, the endpoint devices 304 may include an inputdevice (not shown), such as a keypad, magnetic card reader, tokeninterface, biometric sensor, and so forth. In certain embodiments, suchendpoint devices 304 may be directly, or indirectly, connected to aparticular facility 752, physical security device 754, system 756, orshared device 758. As an example, the endpoint device 304 may bedirectly connected to an ingress/egress system, such as an electroniclock on a door or an access gate of a parking garage. As anotherexample, the endpoint device 304 may be indirectly connected to aphysical security device 754 through a dedicated security network.

In certain embodiments, the security analytics system 118 may beimplemented to perform various risk-adaptive protection operations.Risk-adaptive, as used herein, broadly refers to adaptively respondingto risks associated with an electronically-observable entity behavior.In various embodiments, the security analytics system 118 may beimplemented to perform certain risk-adaptive protection operations bymonitoring certain entity behaviors, assess the corresponding risk theymay represent, individually or in combination, and respond with anassociated response. In certain embodiments, such responses may be basedupon contextual information, described in greater detail herein,associated with a given entity behavior.

In certain embodiments, various information associated with a userentity profile 602, likewise described in greater detail herein, may beused to perform the risk-adaptive protection operations. In certainembodiments, the user entity profile 602 may include user profileattributes 604, user behavior factors 610, user mindset factors 622, ora combination thereof. In these embodiments, the information associatedwith a user entity profile 602 used to perform the risk-adaptiveprotection operations is a matter of design choice.

In certain embodiments, the security analytics system 118 may beimplemented as a stand-alone system. In certain embodiments, thesecurity analytics system 118 may be implemented as a distributedsystem. In certain embodiment, the security analytics system 118 may beimplemented as a virtual system, such as an instantiation of one or morevirtual machines (VMs). In certain embodiments, the security analyticssystem 118 may be implemented as a security analytics service 764. Incertain embodiments, the security analytics service 764 may beimplemented in a cloud environment familiar to those of skill in theart. In various embodiments, the security analytics system 118 may usedata stored in a repository of security analytics data 680, entitybehavior catalog data 690, entity identifier data 670, and event data672, or a combination thereof, in the performance of certain securityanalytics operations, described in greater detail herein. Those of skillin the art will recognize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

FIG. 8 is a simplified block diagram showing the mapping of an event toa security vulnerability scenario implemented in accordance with anembodiment of the invention. In certain embodiments, an entity behaviorcatalog (EBC) system 120 may be implemented to identify a securityrelated activity, described in greater detail herein. In certainembodiments, the security related activity may be based upon anobservable, likewise described in greater detail herein. In certainembodiments, the observable may include event information correspondingto electronically-observable behavior enacted by an entity. In certainembodiments, the event information corresponding toelectronically-observable behavior enacted by an entity may be receivedfrom an electronic data source, such as the EBC data sources 810 shownin FIGS. 8, 15, 16 b, and 17.

In certain embodiments, as likewise described in greater detail herein,the EBC system 120 may be implemented to identify a particular event ofanalytic utility by analyzing an associated security related activity.In certain embodiments, the EBC system 120 may be implemented togenerate entity behavior catalog data based upon an identified event ofanalytic utility associated with a particular security related activity.In various embodiments, the EBC system 120 may be implemented toassociate certain entity behavior data it may generate with apredetermined abstraction level, described in greater detail herein.

In various embodiments, the EBC system 120 may be implemented to usecertain EBC data 690 and an associated abstraction level to generate ahierarchical set of entity behaviors 870, described in greater detailherein. In certain embodiments, the hierarchical set of entity behaviors870 generated by the EBC system 120 may represent an associated securityrisk, likewise described in greater detail herein. Likewise, asdescribed in greater detail herein, the EBC system 120 may beimplemented in certain embodiments to store the hierarchical set ofentity behaviors 870 and associated abstraction level information withina repository of EBC data 690. In certain embodiments, the repository ofEBC data 690 may be implemented to provide an inventory of entitybehaviors for use when performing a security operation, likewisedescribed in greater detail herein.

Referring now to FIG. 8, the EBC system 120 may be implemented invarious embodiments to receive certain event information, described ingreater detail herein, corresponding to an event associated with anentity interaction. As used herein, event information broadly refers toany information directly or indirectly related to an event. As likewiseused herein, an event broadly refers to the occurrence of at least oneaction performed by an entity. In certain embodiments, the at least oneaction performed by an entity may include the enactment of an entitybehavior, described in greater detail herein. In certain embodiments,the entity behavior may include an entity's physical behavior, cyberbehavior, or a combination thereof, as likewise described in greaterdetail herein.

Likewise, as used herein, an entity interaction broadly refers to anaction influenced by another action enacted by an entity. As an example,a first user entity may perform an action, such as sending a textmessage to a second user entity, who in turn replies with a response. Inthis example, the second user entity's action of responding isinfluenced by the first user entity's action of sending the textmessage. In certain embodiments, an entity interaction may include theoccurrence of at least one event enacted by one entity when interactingwith another, as described in greater detail herein. In certainembodiments, an event associated with an entity interaction may includeat least one entity attribute, described in greater detail herein, andat least one entity behavior, likewise described in greater detailherein.

In certain embodiments, an entity attribute and an entity behavior maybe respectively abstracted to an entity attribute 872 and an entitybehavior 874 abstraction level. In certain embodiments, an entityattribute 872 and an entity behavior 874 abstraction level may then beassociated with an event 876 abstraction level. In certain embodiments,the entity attribute 872, entity behavior 874, and event 876 abstractionlevels may in turn be associated with a corresponding entity behaviorhierarchy 870, as described in greater detail herein.

In various embodiments, the event information may be received fromcertain EBC data sources 810, such as a user 802 entity, an endpoint 804non-user entity, a network 806 non-user entity, or a system 808 non-userentity. In certain embodiments, one or more events may be associatedwith a particular entity interaction. As an example, as shown in FIG. 8,one or more events i+n 812 may be associated with a user/device 730interaction between a user 802 entity and an endpoint 904 non-userentity. Likewise, one or more events j+n 814 may be associated with auser/network 742 interaction between a user 802 entity and a network 806non-user entity. As likewise shown in FIG. 8, one or more events k+n 916816 may be associated with a user/resource 748 interaction between auser 802 entity and a system 808 non-user entity.

In certain embodiments, details of an event, such as events i+n 812, j+n814, and k+n 816, may be included in their associated event information.In various embodiments, as described in greater detail herein, analyticutility detection operations may be performed on such event informationto identify events of analytic utility. In various embodiments, certainevent information associated with an event determined to be of analyticutility may be used to derive a corresponding observable. As usedherein, an observable broadly refers to an event of analytic utilitywhose associated event information may include entity behavior that maybe anomalous, abnormal, unexpected, malicious, or some combinationthereof, as described in greater detail herein.

As an example, the details contained in the event informationrespectively corresponding to events i+n 812, j+n 814, and k+n 816 maybe used to derive observables i+n 822, j+n 824, and k+n 826. In certainembodiments, the resulting observables i+n 822, j+n 824, and k+n 826 maythen be respectively associated with a corresponding observable 878abstraction level. In certain embodiments, the observable 878abstraction level may in turn be associated with a corresponding entitybehavior hierarchy 870, as described in greater detail herein.

In certain embodiments, the resulting observables may in turn beprocessed to generate an associated security related activity. As usedherein, a security related activity broadly refers to an abstracteddescription of an interaction between two entities, described in greaterdetail herein, which may represent anomalous, abnormal, unexpected, ormalicious entity behavior. For example, observables i+n 822, j+n 824,and k+n 826 may in turn be processed to generate corresponding securityrelated activities i 832, j 834, and k 836. In certain embodiments, theresulting security related activities, i 832, j 834, and k 836 may thenbe respectively associated with a corresponding security relatedactivity 880 abstraction level. In certain embodiments, the securityrelated activity 880 abstraction level may in turn be associated with acorresponding entity behavior hierarchy 870, as described in greaterdetail herein.

In various embodiments, sessionization and fingerprint generationoperations 820, described in greater detail herein, may be performed toassociate certain events, observables, and security related activities,or a combination thereof, with a corresponding session, likewisedescribed in greater detail herein. As an example, events i+n 812, j+n814, k+n 816, observables i+n 822, j+n 824, k+n 826, and securityrelated activities i 832, j 834, k 836 may be associated withcorresponding sessions. In certain embodiments, a security relatedactivity may be processed with associated contextual information,described in greater detail herein, to generate a corresponding EBPelement.

For example, security related activities i 832, j 834, and k 836 may beprocessed with associated contextual information to generatecorresponding EBP elements i 842, j 844, and k 846. In variousembodiments, the resulting EBP elements i 842, j 844, and k 846 may thenbe associated with a corresponding EBP element 882 abstraction level. Incertain embodiments, the EBP element 882 abstraction level may in turnbe associated with a corresponding entity behavior hierarchy 870, asdescribed in greater detail herein.

In certain embodiments, EBP generation and modification 840 operationsmay be performed to associate one or more EBP elements with a particularEBP 638. As an example, EBP elements i 842, j 844, and k 946 may beassociated with a particular EBP 638, which may likewise be respectivelyassociated with the various entities involved in the user/device 730,user/network 742, or user/resource 748 interactions. In theseembodiments, the method by which the resulting EBP elements i 842, j844, and k 846 are associated with a particular EBP 638 is a matter ofdesign choice. In certain embodiments, the EBP 638 may likewiseassociated with an EBP 884 abstraction level. In certain embodiments,the EBP 884 abstraction level may in turn be associated with acorresponding entity behavior hierarchy 870, as described in greaterdetail herein.

In various embodiments, the resulting EBP 638 may be used in theperformance of security risk use case association 850 operations toidentify one or more security risk use cases that match certain entitybehavior information stored in the EBP 638. As used herein, a securityrisk use case broadly refers to a set of security related activitiesthat create a security risk narrative that can be used to adaptivelydraw inferences, described in greater detail herein, from entitybehavior enacted by a particular entity. In certain of theseembodiments, the entity behavior information may be stored within theEBP 638 in the form of an EBP element, a security related activity, anobservable, or an event, or a combination thereof. In certainembodiments, identified security risk use cases may then be associatedwith a security risk use case 886 abstraction level. In certainembodiments, the security risk use case 886 abstraction level may inturn be associated with a corresponding entity behavior hierarchy 870,as described in greater detail herein.

In certain embodiments, the results of the security risk use caseassociation 850 operations may in turn be used to perform securityvulnerability scenario inference 860 operations to associate one or moresecurity risk use cases with one or more security vulnerabilityscenarios. As used herein, a security vulnerability scenario broadlyrefers to a grouping of one or more security risk use cases thatrepresent a particular class of security vulnerability. In certainembodiments, the associated security vulnerability scenarios may then beassociated with a security vulnerability scenario 888 abstraction level.In certain embodiments, the security vulnerability scenario 888abstraction level may in turn be associated with a corresponding entitybehavior hierarchy 870, as described in greater detail herein.

In various embodiments, certain event information associated with eventsi+n 812, j+n 814, and k+n 816 and certain observable informationassociated with observables i+n 822, j+n 824, and k+n 826 may be storedin a repository of EBC data 690. In various embodiments, certainsecurity related activity information associated with security relatedactivities i 832, j 834, and k 836 and EBP elements i 842, j 844, and k846 may likewise be stored in the repository of EBC data 690. Likewise,in various embodiments, certain security risk use case association andsecurity vulnerability scenario association information respectivelyassociated with the performance of security risk use case association850 and security vulnerability scenario inference 860 operations may bestored in the repository of EBC data 690.

FIG. 9 is a simplified block diagram of the generation of a session anda corresponding session-based fingerprint implemented in accordance withan embodiment of the invention. In certain embodiments, an observable906 may be derived from an associated event, as described in greaterdetail herein. In certain embodiments, one or more observables 906 maybe processed to generate a corresponding security related activity 908.In certain embodiments, one or more security related activities 908 maythen be respectively processed to generate a corresponding activitysession 910. In turn, the session 910 may be processed in certainembodiments to generate a corresponding session fingerprint 912. Incertain embodiments, the resulting activity session 910 and itscorresponding session fingerprint 912, individually or in combination,may then be associated with a particular entity behavior profile (EBP)element 980. In certain embodiments the EBP element 980 may in turn beassociated with an EBP 638.

In certain embodiments, intervals in time 904 respectively associatedwith various security related activities 908 may be contiguous. Forexample, as shown in FIG. 9, the intervals in time 904 associated withobservables 906 ‘1’ 914 and ‘2’ 916 may be contiguous. Accordingly, theintervals in time 904 associated with the security related activities908 ‘1’ 918 and ‘2’ 920 respectively generated from observables 906 ‘1’914 and ‘2’ 916 would likewise be contiguous.

As likewise shown in FIG. 9, the resulting security related activities908 ‘1’ 918 and ‘2’ 920 may be processed to generate an associatedactivity session ‘A’ 922, which then may be processed to generate acorresponding session fingerprint ‘A’ 924. In certain embodiments,activity session ‘A’ 922 and its corresponding session fingerprint ‘A’924 may be used to generate a new entity behavior profile (EBP) element980 ‘A’ 926. In certain embodiments, EBP element 980 ‘A’ 926 generatedfrom activity session 910 ‘A’ 922 and its corresponding sessionfingerprint 912 ‘A’ 924 may be associated with an existing EBP 638.

To provide an example, a user may enact various observables 906 ‘1’ 914to update sales forecast files, followed by the enactment of variousobservables 906 ‘2’ 1016 to attach the updated sales forecast files toan email, which is then sent to various co-workers. In this example, theenactment of observables 906 ‘1’ 914 and ‘2’ 916 result in thegeneration of security related activities 908 ‘1’ 918 and ‘2’ 920, whichin turn are used to generate activity session 910 ‘A’ 922. In turn, theresulting activity session 910 ‘A’ 922 is then used to generate itscorresponding session-based fingerprint 912 ‘A’ 924. To continue theexample, activity session 910 ‘A’ 922 is associated with securityrelated activities 908 ‘1’ 918 and ‘2’ 920, whose associated intervalsin time 904 are contiguous, as they are oriented to the updating anddistribution of sales forecast files via email.

Various aspects of the invention reflect an appreciation that a user mayenact certain entity behaviors on a recurring basis. To continue thepreceding example, a user may typically update sales forecast files anddistribute them to various co-workers every morning between 8:00 AM and10:00 AM. Accordingly, the activity session 910 associated with such arecurring activity may result in a substantively similar sessionfingerprint 912 week-by-week. However, a session fingerprint 912 for thesame session 910 may be substantively different should the user happento send an email with an attached sales forecast file to a recipientoutside of their organization. Consequently, a session fingerprint 912that is inconsistent with session fingerprints 912 associated with pastactivity sessions 910 may indicate anomalous, abnormal, unexpected ormalicious behavior.

In certain embodiments, two or more activity sessions 910 may benoncontiguous, but associated. In certain embodiments, an activitysession 910 may be associated with two or more sessions 910. In certainembodiments, an activity session 910 may be a subset of another activitysession 910. As an example, as shown in FIG. 9, the intervals in time904 respectively associated with observables 906 ‘3’ 914 and ‘6’ 932 maybe contiguous. Likewise, the intervals in time 904 associated withobservables 906 ‘4’ 936 and ‘5’ 938 may be contiguous.

Accordingly, the intervals in time 904 associated with the securityrelated activities 908 ‘4’ 936 and ‘5’ 938 respectively generated fromobservables 906 ‘4’ 928 and ‘5’ 930 would likewise be contiguous.However, the intervals in time 904 associated with security relatedactivities 908 ‘4’ 936 and ‘5’ 938 would not be contiguous with theintervals in time respectively associated with security relatedactivities 908 ‘3’ 934 and ‘6’ 940.

As likewise shown in FIG. 9, the resulting security related activities908 ‘3’ 934 and ‘6’ 940 may be respectively processed to generatecorresponding sessions ‘B’ 942 and ‘D’ 946, while security relatedactivities 908 ‘4’ 936 and ‘5’ 938 may be processed to generate activitysession 910 ‘C’ 944. In turn, activity sessions 910 ‘B’ 942, ‘C’ 944,and ‘D’ 946 are then respectively processed to generate correspondingsession-based fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952.

Accordingly, the intervals of time 904 respectively associated withactivity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952, arenot contiguous. Furthermore, in this example activity sessions 910 ‘B’942, ‘C’ 944, and ‘D’ 946, and their corresponding session fingerprints912 ‘B’ 948, ‘C’ 950 and ‘D’ 952, are not associated with the EBP 638.Instead, as shown in FIG. 9, activity sessions 910 ‘B’ 942, ‘C’ 944, and‘D’ 946 are processed to generate activity session 910 ‘E’ 954 andsession fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952 are processed togenerate session fingerprint 912 ‘E’ 956. In certain embodiments,activity session ‘E’ 954 and its corresponding session fingerprint ‘E’956 may be used to generate a new EBP element 980 ‘E’ 958. In certainembodiments, EBP element 980 ‘E’ 958 generated from activity session 910‘E’ 954 and its corresponding session fingerprint 912 ‘E’ 956 may beassociated with an existing EBP 638.

Accordingly, session 910 ‘E’ 1054 is associated with activity sessions910 ‘B’ 942, ‘C’ 944, and ‘D’ 946. Likewise, sessions 910 ‘B’ 942, ‘C’944, and ‘D’ 946 are subsets of session 910 ‘E’ 954. Consequently, whilethe intervals of time respectively associated with activity sessions 910‘B’ 942, ‘C’ 944, and ‘D’ 946, and their corresponding sessionfingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952 may not be contiguous,they are associated as they are respectively used to generate session910 ‘E’ 954 and its corresponding session fingerprint 912 ‘E’ 1056.

To provide an example, a user plans to attend a meeting scheduled for10:00 AM at a secure facility owned by their organization to review aproject plan with associates. However, the user wishes to arrive earlyto prepare for the meeting. Accordingly, they arrive at 9:00 AM and usetheir security badge to authenticate themselves and enter the facility.In this example, the enactment of observables 906 ‘3’ 926 may correspondto authenticating themselves with their security badge and gainingaccess to the facility. As before, observables 906 ‘3’ 926 may be usedto generate a corresponding security related activity 908 ‘3’ 934. Inturn, the security related activity 908 ‘3’ 934 may then be used togenerate session 910 ‘B’ 942, which is likewise used in turn to generatea corresponding session fingerprint 912 ‘B’ 948.

The user then proceeds to a conference room reserved for the meetingscheduled for 10:00 AM and uses their time alone to prepare for theupcoming meeting. Then, at 10:00 AM, the scheduled meeting begins,followed by the user downloading the current version of the projectplan, which is then discussed by the user and their associate for a halfhour. At the end of the discussion, the user remains in the conferenceroom and spends the next half hour making revisions to the project plan,after which it is uploaded to a datastore for access by others.

In this example, observables 906 ‘4’ 928 may be associated with the userdownloading and reviewing the project plan and observables 906 ‘5’ 930may be associated with the user making revisions to the project plan andthen uploading the revised project plan to a datastore. Accordingly,behavior elements 906 ‘4’ 928 and ‘5’ 930 may be respectively used togenerate security related activities 908 ‘4’ 936 and ‘5’ 938. In turn,the security related activities 908 ‘4’ 936 and ‘5’ 938 may then be usedto generate session 910 ‘C’ 944, which may likewise be used in turn togenerate its corresponding session-based fingerprint 912 ‘C’ 950.

To continue the example, the user may spend the next half hourdiscussing the revisions to the project plan with a co-worker.Thereafter, the user uses their security badge to exit the facility. Incontinuance of this example, observables 906 ‘6’ 932 may be associatedwith the user using their security badge to leave the secure facility.Accordingly, observables 906 ‘6’ 932 may be used to generate acorresponding security related activity 908 ‘6’ 940, which in turn maybe used to generate a corresponding session 910 ‘D’ 946, which likewisemay be used in turn to generate a corresponding session fingerprint 912‘D’ 952.

In this example, the intervals of time 904 respectively associated withactivity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session fingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’ 952,are not contiguous. However they may be considered to be associated astheir corresponding observables 906 ‘3’ 926, ‘4’ 928, ‘5’ 930, and ‘6’932 all have the common attribute of having been enacted within thesecure facility. Furthermore, security related activities 908 ‘4’ 936and ‘5’ 938 may be considered to be associated as their correspondingobservables 906 have the common attribute of being associated with theproject plan.

Accordingly, while the intervals of time 904 respectively associatedwith activity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session-based fingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’952, may not be contiguous, they may be considered to be associated.Consequently, sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946 may beconsidered to be a subset of session 910 ‘E’ 954 and session-basedfingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’ 952 may be considered to be asubset of session-based fingerprint 912 ‘E’ 956.

In certain embodiments, the interval of time 904 corresponding to afirst activity session 910 may overlap an interval of time 904corresponding to a second activity session 910. For example, observables906 ‘7’ 958 and ‘8’ 960 may be respectively processed to generatesecurity related activities 908 ‘7’ 962 and ‘8’ 964. In turn, theresulting security related activities 908 ‘7’ 962 and ‘8’ 964 arerespectively processed to generate corresponding activity sessions 910‘F’ 966 and ‘G’ 968. Sessions The resulting activity sessions 910 ‘F’966 and ‘G’ 968 are then respectively processed to generatecorresponding session-based fingerprints 912 ‘F’ 970 and ‘G’ 972.

However, in this example activity sessions 910 ‘F’ 966 and ‘G’ 968, andtheir corresponding session fingerprints 912 ‘F’ 970 and ‘G’ 972, arenot associated with the EBP 638. Instead, as shown in FIG. 9, activitysessions 910 ‘F’ 966 and ‘G’ 968 are processed to generate activitysession 910 ‘E’ 954 and session fingerprints 912 ‘F’ 970 and ‘G’ 972 areprocessed to generate session fingerprint 912 ‘H’ 976. In certainembodiments, activity session ‘H’ 974 and its corresponding sessionfingerprint ‘H’ 976 may be used to generate a new EBP element 980 ‘H’978. In certain embodiments, EBP element 980 ‘H’ 978 generated fromactivity session 910 ‘E’ 974 and its corresponding session fingerprint912 ‘E’ 976 may be associated with an existing EBP 638.

Accordingly, the time 904 interval associated with activity session 910‘F’ 966 and its corresponding session fingerprint 912 ‘F’ 970 overlapswith the time interval 904 associated with activity session 910 ‘G’ 968and its corresponding session fingerprint 912 ‘G’ 972. As a result,activity sessions 910 ‘F’ 966 and ‘G’ 968 are subsets of activitysession 910 ‘H’ 974. Consequently, while the intervals of timerespectively associated with activity sessions 910 ‘F’ 966 and ‘G’ 968,and their corresponding session fingerprints 912 ‘F’ 970 and ‘G’ 972 mayoverlap, they are associated as they are respectively used to generateactivity session 910 ‘H’ 974 and its corresponding session fingerprint912 ‘H’ 976.

To provide an example, a user may decide to download various images forplacement in an online publication. In this example, observables 906 ‘7’958 may be associated with the user iteratively searching for, anddownloading, the images they wish to use in the online publication.However, the user may not begin placing the images into the onlinepublication until they have selected and downloaded the first few imagesthey wish to use.

To continue the example, observables 906 ‘8’ may be associated with theuser placing the downloaded images in the online publication.Furthermore, the placement of the downloaded images into the onlinepublication may begin a point in time 904 subsequent to when the userbegan to download the images. Moreover, the downloading of the imagesmay end at a point in time 904 sooner than when the user completes theplacement of the images in the online publication.

In continuance of the example, observables 906 ‘7’ 958 and ‘8’ 960 maybe respectively processed to generate security related activities 908‘7’ 962 and ‘8’ 964, whose associated intervals of time 904 overlap oneanother. Accordingly, the intervals in time 904 associated with activitysessions 910 ‘F’ 966 and ‘G’ 968 will likewise overlap one another asthey are respectively generated from security related activities 908 ‘7’962 and ‘8’ 964.

Consequently, while the intervals of time 904 respectively associatedwith activity sessions 910 ‘F’ 966 and ‘G’ 968, and their correspondingsession fingerprints 912 ‘F’ 970 and ‘G’ 972, may overlap, they may beconsidered to be associated as they both relate to the use of images forthe online publication. Accordingly, activity sessions 910 ‘F’ 1066 and‘G’ 968 may be considered to be a subset of activity session 910 ‘H’ 974and session fingerprints 912 ‘F’ 970 and ‘G’ 972 may be considered to bea subset of session fingerprint 912 ‘H’ 976.

FIG. 10 is a generalized flowchart of session fingerprint generationoperations performed in accordance with an embodiment of the invention.In this embodiment, activity session fingerprint generation operationsare begun in step 1002, followed by the selection of an entity in step1004 for associated entity behavior profile (EBP) element generation. Asused herein, an EBP element broadly refers to any data element stored inan EBP, as described in greater detail herein. In various embodiments,an EBP element may be used to describe a particular aspect of an EBP,such as certain entity behaviors enacted by an entity associated withthe EBP. Ongoing monitoring operations are then performed in step 1006to monitor the selected entity's behavior to detect the occurrence of anevent, described in greater detail herein.

A determination is then made in step 1008 whether an event has beendetected. If not, then a determination is made in step 1026 whether tocontinue monitoring the entity's behavior to detect an event. If so,then the process is continued, proceeding with step 1006. Otherwise,session fingerprint generation operations are ended in step 1028.However, if it was determined in step 1008 that an event was detected,then event data associated with the detected event is processed todetermine whether the event is of analytic utility, as described ingreater detail herein.

A determination is then made in step 1012 to determine whether the eventis of analytic utility. If not, then the process is continued,proceeding with 1026. Otherwise, an observable, described in greaterdetail herein, is derived from the event in step 1014. The resultingobservable is then processed with associated observables in step 1016,as likewise described in greater detail herein, to generate a securityrelated activity. As likewise described in greater detail herein, theresulting security related activity is then processed in step 1018 withassociated security related activities to generate an activity session.

In turn, the resulting activity session is then processed in step 1020to generate a corresponding session fingerprint. The resulting sessionfingerprint is then processed with its corresponding activity session instep 1022 to generate an associated EBP element. The resulting EBPelement is then added to an EPB associated with the entity in step 1024and the process is then continued, proceeding with step 1026.

FIG. 11 is simplified block diagram of process flows associated with theoperation of an entity behavior catalog (EBC) system implemented inaccordance with an embodiment of the invention. In certain embodiments,the EBC system 120 may be implemented to define and manage an entitybehavior profile (EBP) 638, as described in greater detail herein. Incertain embodiments, the EBP 638 may be implemented to include a userentity profile 602, a user entity mindset profile 632, a non-user entityprofile 634, and an entity state 636, or a combination thereof, aslikewise described in greater detail herein.

In certain embodiments, the EBC system 120 may be implemented use aparticular user entity profile 602 in combination with a particularentity state 638 to generate an associated user entity mindset profile632, likewise as described in greater detail herein. In certainembodiments, the EBC system 120 may be implemented to use the resultinguser entity mindset profile 632 in combination with its associated userentity profile 602, non-user entity profile 634, and entity state 638,or a combination thereof, to detect entity behavior of analytic utility.In various embodiments, the EBC system 120 may be implemented to performEBP management 1124 operations to process certain entity attribute andentity behavior information, described in greater detail herein,associated with defining and managing an EBP 638.

As used herein, entity attribute information broadly refers toinformation associated with a particular entity. In various embodiments,the entity attribute information may include certain types of content.In certain embodiments, such content may include text, unstructureddata, structured data, graphical images, photographs, audio recordings,video recordings, biometric information, and so forth. In certainembodiments, the entity attribute information may include metadata. Incertain embodiments, the metadata may include entity attributes, whichin turn may include certain entity identifier types or classifications.

In certain embodiments, the entity attribute information may includeentity identifier information. In various embodiments, the EBC system120 may be implemented to use certain entity identifier information toascertain the identity of an associated entity at a particular point intime. As used herein, entity identifier information broadly refers to aninformation element associated with an entity that can be used toascertain or corroborate the identity of its corresponding entity at aparticular point in time. In certain embodiments, the entity identifierinformation may include user authentication factors, user entity 602 andnon-user entity 634 profile attributes, user and non-user entitybehavior factors, user entity mindset factors, information associatedwith various endpoint and edge devices, networks, and resources, or acombination thereof.

In certain embodiments, the entity identifier information may includetemporal information. As used herein, temporal information broadlyrefers to a measure of time (e.g., a date, timestamp, etc.), a measureof an interval of time (e.g., a minute, hour, day, etc.), or a measureof an interval of time (e.g., two consecutive weekdays days, or betweenJun. 3, 2017 and Mar. 4, 2018, etc.). In certain embodiments, thetemporal information may be associated with an event associated with aparticular point in time. As used herein, such a temporal event broadlyrefers to an occurrence, action or activity enacted by, or associatedwith, an entity at a particular point in time.

Examples of such temporal events include making a phone call, sending atext or an email, using a device, such as an endpoint device, accessinga system, and entering a physical facility. Other examples of temporalevents include uploading, transferring, downloading, modifying, ordeleting data, such as data stored in a datastore, or accessing aservice. Yet other examples of temporal events include interactionsbetween two or more users, interactions between a user and a device,interactions between a user and a network, and interactions between auser and a resource, whether physical or otherwise. Yet still otherexamples of temporal events include a change in name, address, physicallocation, occupation, position, role, marital status, gender,association, affiliation, or assignment.

As likewise used herein, temporal event information broadly refers totemporal information associated with a particular event. In variousembodiments, the temporal event information may include certain types ofcontent. In certain embodiments, such types of content may include text,unstructured data, structured data, graphical images, photographs, audiorecordings, video recordings, and so forth. In certain embodiments, thetemporal event information may include metadata. In various embodiments,the metadata may include temporal event attributes, which in turn mayinclude certain entity identifier types or classifications, described ingreater detail herein.

In certain embodiments, the EBC system 120 may be implemented to useinformation associated with such temporal resolution of an entity'sidentity to assess the risk associated with a particular entity, at aparticular point in time, and respond with a security operation 1128,described in greater detail herein. In certain embodiments, the EBCsystem 120 may be implemented to respond to such assessments in order toreduce operational overhead and improve system efficiency whilemaintaining associated security and integrity. In certain embodiments,the response to such assessments may be performed by a securityadministrator. Accordingly, certain embodiments of the invention may bedirected towards assessing the risk associated with the affirmativeresolution of the identity of an entity at a particular point in time incombination with its behavior and associated contextual information.Consequently, the EBC system 120 may be more oriented in variousembodiments to risk adaptation than to security administration.

Referring now to FIG. 11, in certain embodiments, EBC system 120operations are begun with the receipt of information associated with aninitial event i 1102. In certain embodiments, information associatedwith an initial event i 1102 may include user entity profile 602attributes, user behavior factors, user entity mindset factors, entitystate information, contextual information, all described in greaterdetail herein, or a combination thereof. In various embodiments, certainuser entity profile 602 data, user entity mindset profile 632 data,non-user entity profile 634 data, entity state 636 data, contextualinformation, and temporal information stored in a repository of EBC data690 may be retrieved and then used to perform event enrichment 1108operations to enrich the information associated with event i 1102.

Analytic utility detection 1112 operations are then performed on theresulting enriched event i 1102 to determine whether it is of analyticutility. If so, then it is derived as an observable 906, described ingreater detail herein. In certain embodiments, event i+1 1104 throughevent i+n 1106, may in turn be received by the EBC system 120 and beenriched 1008. Analytic utility detection 1112 operations are thenperformed on the resulting enriched event i+1 1104 through event i+n1106 to determine whether they are of analytic utility. Observables 906are then derived from those that are.

In certain embodiments, security related activity abstraction 1114operations may be performed on the resulting observables 906corresponding to events i 1102, i+1 1104, i+n 1106 to generate anassociated security related activity 908, described in greater detailherein. In various embodiments, a security related activity 908 may beexpressed in a Subject Action Object format and associated withobservables 906 resulting from event information provided by variousreceived from certain EBC data sources, likewise described in greaterdetail herein. In certain embodiments, a security related activityabstraction 1114 operation may be performed to abstract away EBC datasource-specific knowledge and details when expressing an entitybehavior. For example, rather than providing the details associated witha “Windows:4624” non-user entity event, its details may be abstracted to“User Login To Device” security related activity 908.

In various embodiments, sessionization and fingerprint 820 operations,described in greater detail herein, may be performed on eventinformation corresponding to events i 1102, i+1 1104, i+n 1106, theircorresponding observables 906, and their associated security relatedactivities 908, or a combination thereof, to generate sessioninformation. In various embodiments, the resulting session informationmay be used to associate certain events i 1102, i+1 1104, i+n 1106, ortheir corresponding observables 906, or their corresponding securityrelated activities 908, or a combination thereof, with a particularsession.

In certain embodiments, as likewise described in greater detail herein,one or more security related activities 908 may in turn be associatedwith a corresponding EBP element. In various embodiments, thepreviously-generated session information may be used to associate theone or more security related activities 908 with a particular EBPelement. In certain embodiments, the one or more security relatedactivities 908 may be associated with its corresponding EBP elementthrough the performance of an EBP management 1124 operation. Likewise,in certain embodiments, one or more EBP elements may in turn beassociated with the EBP 638 through the performance of an EBP management1124 operation.

In various embodiments, certain contextualization information stored inthe repository of EBC data 690 may be retrieved and then used to performentity behavior contextualization 1118 operations to provide entitybehavior context, based upon the entity's user entity profile 602, ornon-user entity profile 634, and its associated entity state 638. Invarious embodiments, security risk use case association 1118 operationsmay be performed to associate an EBP 638 with a particular security riskuse case. In certain embodiments, the results of thepreviously-performed entity behavior contextualization 1118 operationsmay be used to perform the security risk use case association 850operations.

In various embodiments, security vulnerability scenario inference 860operations may be performed to associate a security risk use case with aparticular security vulnerability scenario, described in greater detailherein. In various embodiments, certain observables 906 derived fromevents of analytical utility may be used to perform the securityvulnerability scenario inference 860 operations. In various embodiments,certain entity behavior contexts resulting from the performance of theentity behavior contextualization 1118 operations may be used to performthe security vulnerability scenario inference 860 operations.

In certain embodiments, entity behavior meaning derivation 1126operations may be performed on the security vulnerability behaviorscenario selected as a result of the performance of the securityvulnerability scenario inference 860 operations to derive meaning fromthe behavior of the entity. In certain embodiments, the entity behaviormeaning derivation 1126 operations may be performed by analyzing thecontents of the EBP 638 in the context of the security vulnerabilitybehavior scenario selected as a result of the performance of thesecurity vulnerability scenario inference 860 operations. In certainembodiments, the derivation of entity behavior meaning may includeinferring the intent of an entity associated with event event i 1102 andevent i+1 1104 through event i+n 1106.

In various embodiments, performance of the entity behavior meaningderivation 1126 operations may result in the performance of a securityoperation 1128, described in greater detail herein. In certainembodiments, the security operation 1128 may include a cyber kill chain1130 operation, or a risk-adaptive protection 1132 operation, or both.In certain embodiments, the cyber kill chain 1130 operation may beperformed to disrupt the execution of a cyber kill chain, described ingreater detail herein. In certain embodiments, the risk-adaptiveprotection 1132 operation may include adaptively responding with anassociated risk-adaptive response, as described in greater detailherein.

In various embodiments, the security operation 1128 may include certainrisk mitigation operations being performed by a security administrator.As an example, performance of the security operation 1128 may result ina notification being sent to a security administrator alerting them tothe possibility of suspicious behavior. In certain embodiments, thesecurity operation 1128 may include certain risk mitigation operationsbeing automatically performed by a security analytics system or service.As an example, performance of the security operation 1128 may result ina user's access to a particular system being disabled if an attemptedaccess occurs at an unusual time or from an unknown device.

In certain embodiments, meaning derivation information associated withevent i 1102 may be used to update the user entity profile 602 ornon-user entity profile 634 corresponding to the entity associated withevent event i 1102. In certain embodiments, the process is iterativelyrepeated, proceeding with meaning derivation information associated withevent i+1 1104 through event i+n 1106. From the foregoing, skilledpractitioners of the art will recognize that a user entity profile 602,or a non-user entity profile 634, or the two in combination, asimplemented in certain embodiments, not only allows the identificationof events associated with a particular entity that may be of analyticutility, but also provides higher-level data that allows for thecontextualization of observed events. Accordingly, by viewing individualsets of events both in context and with a view to how they may be ofanalytic utility, it is possible to achieve a more nuanced andhigher-level comprehension of an entity's intent.

FIG. 12 is a table showing components of an entity behavior profile(EBP) implemented in accordance with an embodiment of the invention. Invarious embodiments, an EBP 638 may be implemented to certain includeentity attributes 1204 behavioral models 1206, and inferences 1208,along with entity state 636. In certain embodiments, an EBP's 638 entitystate 636 may be short-term, or reflect the state of an entity at aparticular point or interval in time. In certain embodiments, an EBP's638 entity state 636 may be long-term, or reflect the state of an entityat recurring points or intervals in time.

In certain embodiments, an EBP's 638 associated entity attributes 1204may be long-lived. As an example, a particular user entity may have aname, an employee ID, an assigned office, and so forth, all of which arefacts rather than insights. In certain embodiments, a particular entitystate 636 may be sufficiently long-termed to be considered an entityattribute 1204. As an example, a first user and a second user may bothhave an entity state 636 of being irritable. However, the first user mayhave a short-term entity state 636 of being irritable on an infrequentbasis, while the second user may have a long-term entity state 636 of beirritable on a recurring basis. In this example, the long-term entitystate 636 of the second user being irritable may be considered to be anentity attribute 1204. In various embodiments, the determination of whatconstitutes an entity state 636 and an entity attribute 1204 is a matterof design choice. In certain embodiments, various knowledgerepresentation approaches may be implemented in combination with anentity behavior catalog (EBC) system to understand the ontologicalinterrelationship of entity attributes 1104 one or more EBP's 638 maycontain. In these embodiments, the method by which certain entityattributes 1204 are selected to be tracked by an EBC system, and themethod by which they are managed within a corresponding EBP 638, is amatter of design choice.

In certain embodiments, the ATP 638 evolves over time as new events andentity behavior is detected. In certain embodiments, an ATP's 638associated behavioral models 1206, and thus the ATP 638 itself mayevolve over time. In certain embodiments, an ATP's 638 behavioral models1206 may be used by an ATP system to provide insight into how unexpecteda set of events may be. As an example, a behavioral model 1206 mayinclude information related to where a particular user entity works,which devices they may use and locations they may login from, who theymay communicate with, and so forth. Certain embodiments of the inventionreflect an appreciation that such behavioral models 1206 can be usefulwhen comparing observed user and non-user entity behaviors to pastobservations in order to determine how unusual a particular entitybehavior may be.

For example, a user may have more than one EBP 638 associated with aparticular channel, which as used herein broadly refers to a mediumcapable of supporting the electronic observation of a user or non-userbehavior, such as a keyboard, a network, a video stream, and so forth.To continue the example, the user may have a particular set of people hesends emails to from his desktop computer, and does so in an orderly andmethodical manner, carefully choosing his words, and writing longer thanaverage messages compared to his peers. Consequently, analysis of suchan email message will likely indicate it was authored by the user andnot someone else.

However, the same user may also send emails from a second channel, whichis his mobile telephone. When using his mobile telephone, the user'semails are typically short, contains typos and emojis, and his writingstyle is primarily limited to simple confirmations or denials.Consequently, analysis of one such email would likely not reveal whetherthe user was the author or not, due to its brevity. Accordingly, the useof the same channel, which in this example is email, demonstrates theuse of different devices will likely generate different behavioralmodels 1206, which in turn could affect the veracity of associatedinferences 1208.

In certain embodiments, a behavioral model 1206 may be implemented as asession-based fingerprint. As used herein, a session-based fingerprintbroadly refers to a unique identifier of an enactor of user or non-userbehavior associated with a session. In certain embodiments, thesession-based fingerprint may be implemented to determine how unexpectedan event may be, based upon an entity's history as it relates to therespective history of their peer entities. In certain embodiments, thesession-based fingerprint may be implemented to determine whether anentity associated with a particular session is truly who they or itclaims to be or if they are being impersonated. In certain embodiments,the session-based fingerprint may be implemented to determine whether aparticular event, or a combination thereof, may be of analytic utility.In certain embodiments, the session-based fingerprint may include a riskscore, be used to generate a risk score, or a combination thereof.

As likewise used herein, a fingerprint, as it relates to a session,broadly refers to a collection of information providing one or moredistinctive, characteristic indicators of the identity of an enactor ofone or more corresponding user or non-user entity behaviors during thesession. In certain embodiments, the collection of information mayinclude one or more user or non-user profile elements. A user ornon-user profile element, as used herein, broadly refers to a collectionof user or non-user entity behavior elements, described in greaterdetail herein.

As used herein, inferences 1208 broadly refer to things that can beinferred about an entity based upon observations. In certain embodimentsthe observations may be based upon electronically-observable behavior,described in greater detail herein. In certain embodiments, the behaviormay be enacted by a user entity, a non-user entity, or a combinationthereof. In certain embodiments, inferences 1108 may be used to provideinsight into a user entity's mindset or affective state.

As an example, an inference 1208 may be made that a user is unhappy intheir job or that they are facing significant personal financialpressures. Likewise, based upon the user's observed behavior, aninference 1208 may be made that they are at a higher risk of beingvictimized by phishing schemes due to a propensity for clicking onrandom or risky website links. In certain embodiments, such inferences1208 may be implemented to generate a predictive quantifier of riskassociated with an entity's behavior.

In certain embodiments, entity state 636, described in greater detailherein, may be implemented such that changes in state can beaccommodated quickly while reducing the overall volatility of aparticular EBP 638. As an example, a user may be traveling byautomobile. Accordingly, the user's location is changing quickly.Consequently, location data is short-lived. As a result, while thelocation of the user may not be updated within their associated EBP 638as it changes, the fact their location is changing may prove to beuseful in terms of interpreting other location-based data from othersessions. To continue the example, knowing the user is in the process ofchanging their location may assist in explaining why the user appears tobe in two physical locations at once.

FIG. 13 is a activities table showing analytic utility actions occurringduring a session implemented in accordance with an embodiment of theinvention. In certain embodiments, an entity behavior catalog (EBC)system, described in greater detail herein, may be implemented tocapture and record various entity actions 1304 enacted by an entityduring a session 1302, likewise described in greater detail herein. Incertain embodiments, the actions, and their associated sessions, may bestored in an entity behavior profile (EBP) corresponding to a particularentity. In various embodiments, the EBC system may be implemented toprocess information stored in an EBP to determine, as described ingreater detail herein, which actions 1304 enacted by a correspondingentity during a particular session 1302 may be of analytic utility 1308.

Certain embodiments of the invention reflect an appreciation thatmultiple sessions 1302, each of which may be respectively associatedwith a corresponding entity, may occur within the same interval of time1306. Certain embodiments of the invention likewise reflect anappreciation that a single action of analytic utility 1308 enacted by anentity occurring during a particular interval of time 1306 may notappear to be suspicious behavior by an associated entity. Likewise,certain embodiments of the invention reflect an appreciation that theoccurrence of multiple actions of analytic utility 1308 enacted by anentity during a particular session 1302 may be an indicator ofsuspicious behavior.

Certain embodiments reflect an appreciation that a particular entity maybe associated with two or more sessions 1302 that occur concurrentlyover a period of time 1306. Certain embodiments of the inventionlikewise reflect an appreciation that a single action of analyticutility 1308 enacted by an entity occurring during a first session 1302may not appear to be suspicious. Conversely, certain embodiments of theinvention reflect an appreciation that multiple actions of analyticutility 1308 during a second session 1302 may.

As an example, a user may log into the same system from two different IPaddresses, one associated with their laptop computer and the other theirmobile phone. In this example, entity actions 1204 1304 enacted by theuser using their laptop computer may be associated with a first session1302 (e.g. session ‘2’), and entity actions 1304 enacted by the userusing their mobile phone may be associated with a second session 12021302 (e.g., session ‘3’). To continue the example, only one action ofanalytic utility 1308 may be associated with the first session 1302,while three actions of analytic utility 1308 may be associated with thesecond session 1302. Accordingly, it may be inferred the preponderanceof actions of analytic utility 1308 enacted by the user during thesecond session 1302 may indicate suspicious behavior being enacted withtheir mobile phone.

FIGS. 14a and 14b are a generalized flowchart of the performance ofentity behavior catalog (EBC) system operations implemented inaccordance with an embodiment of the invention. In this embodiment, EBCsystem operations are begun in step 1402, with ongoing operations beingperformed by the EBC system in step 1404 to monitor the receipt of eventinformation to detect the occurrence of an event, described in greaterdetail herein.

A determination is then made in step 1406 to determine whether an eventhas been detected. If not, then a determination is made in step 1436 todetermine whether to continue monitoring the receipt of eventinformation. If so, then the process is continued, proceeding with step1404. If not, then a determination is made in step 1440 whether to endEBC system operations. If not, then the process is continued, proceedingwith step 1404. Otherwise EBC system operations are ended in step 1442.

However, if it was determined in step 1406 that an event was detected,then event enrichment operations, described in greater detail herein,are performed on the event in step 1408. Analytic utility detectionoperations are then performed on the resulting enriched event in step1410 to identify entity behavior of analytic utility, as likewisedescribed in greater detail herein. A determination is then made in step1412 to determine whether the enriched event is associated with entitybehavior of analytic utility. If not, then the process is continued,proceeding with step 1440. Otherwise, an observable is derived from theevent in step 1414, as described in greater detail herein.

The resulting observable is then processed with associated observablesin step 1416 to generate a security related activity, likewise describedin greater detail herein. In turn, the resulting security relatedactivity is processed with associated security related activities instep 1418 to generate an activity session, described in greater detail.Thereafter, as described in greater detail herein, the resultingactivity session is processed in step 1420 to generate a correspondingactivity session. In turn, the resulting activity session is processedwith the activity session in step 1422 to generate an EBP element, whichis then added to an associated EBP in step 1424.

Thereafter, in step 1426, certain contextualization information storedin a repository of EBC data may be retrieved and then used in step 1428to perform entity behavior contextualization operations to generateinferences related to the entity's behavior. The EBP is then processedwith resulting entity behavior inferences in step 1430 to associate theEBP with one or more corresponding risk use cases, as described ingreater detail herein. In turn, the one or more risk use cases are thenassociated in step 1432 with one or more corresponding securityvulnerability scenarios, as likewise described in greater detail herein.

Then, in step 1434, entity behavior meaning derivation operations areperformed on the EBP and each security vulnerability behavior scenarioselected in step 1432 to determine whether the entity's behaviorwarrants performance of a security operation. Once that determination ismade, a subsequent determination is made in step 1436 whether to performa security operation. If not, then the process is continued, proceedingwith step 1440. Otherwise, the appropriate security operation, describedin greater detail herein, is performed in step 1438 and the process iscontinued, proceeding with step 1440.

FIG. 15 shows a functional block diagram of the operation of an entitybehavior catalog (EBC) system implemented in accordance with anembodiment of the invention. In various embodiments, certain EBC-relatedinformation, described in greater detail herein, may be provided byvarious EBC data sources 810, likewise described in greater detailherein. In certain embodiments, the EBC data sources 810 may includeendpoint devices 304, edge devices 202, third party sources 1506, andother 1520 data sources. In certain embodiments, the receipt ofEBC-related information provided by third party sources 1506 may befacilitated through the implementation of one or more Apache NiFiconnectors 1508, familiar to skilled practitioners of the art.

In certain embodiments, activity sessionization and session fingerprintgeneration 1520 operations may be performed on the EBC-relatedinformation provided by the EBC data sources 810 to generate discretesessions. As used herein, activity sessionization broadly refers to theact of turning event-based data into activity sessions, described ingreater detail herein. In these embodiments, the method by which certainEBC-related information is selected to be used in the generation of aparticular activity session, and the method by which the activitysession is generated, is a matter of design choice. As likewise usedherein, an activity session broadly refers to an interval of time duringwhich one or more user or non-user behaviors are respectively enacted bya user or non-user entity.

In certain embodiments, the user or non-user behaviors enacted during anactivity session may be respectively associated with one or more events,described in greater detail herein. In certain embodiments, an activitysession may be implemented to determine whether or not user or non-userbehaviors enacted during the session are of analytic utility. As anexample, certain user or non-user behaviors enacted during a particularactivity session may indicate the behaviors were enacted by an impostor.As another example, certain user or non-user behaviors enacted during aparticular activity session may be performed by an authenticated entity,but the behaviors may be unexpected or out of the norm.

In certain embodiments, two or more activity sessions may be contiguous.In certain embodiments, two or more activity sessions may benoncontiguous, but associated. In certain embodiments, an activitysession may be associated with two or more other activity sessions. Incertain embodiments, an activity session may be a subset of anotheractivity session. In certain embodiments, the interval of timecorresponding to a first activity session may overlap an interval oftime corresponding to a second activity session. In certain embodiments,an activity session may be associated with two or more other activitysessions whose associated intervals of time may overlap one another.Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

The resulting activity sessions and session fingerprints are theningested 1516, followed by the performance of data enrichment 1514operations familiar to those of skill in the art. In certainembodiments, user identifier information (ID) information provided by auser ID management system 1512 may be used to perform the dataenrichment 1514 operations. In various embodiments, certain contextualinformation related to a particular entity behavior or event may be usedto perform the data enrichment 1514 operations. In various embodiments,certain temporal information, such as timestamp information, related toa particular entity behavior or event may be used to perform the dataenrichment 1514 operations. In certain embodiments, a repository of EBCdata 690 may be implemented to include repositories of entity attributedata 694, entity behavior data 695, and behavioral model data 696. Invarious embodiments, certain information stored in the repository ofentity attribute data 694 may be used to perform the data enrichmentoperations 1514.

In certain embodiments, the resulting enriched sessions may be stored inthe repository of entity behavior data 695. In certain embodiments, theresulting enriched sessions may be provided to a risk services 422module, described in greater detail herein. In certain embodiments, aslikewise described in greater detail herein, the risk services 422module may be implemented to generate inferences, risk models, and riskscores, or a combination thereof. In certain embodiments, the resultinginferences, risk models, and risk scores, or a combination thereof, maythen be stored in the repository of entity behavioral model data 696.

In certain embodiments, the risk services 422 module may be implementedto provide input data associated with the inferences, risk models, andrisk scores it may generate to a security policy service 1528. Incertain embodiments, the security policy service 1528 may be implementedto use the inferences, risk models, and risk scores to generate securitypolicies. In turn, the security policy service 1528 may be implementedin certain embodiments to export 1530 the resulting security policies toendpoint agents or devices 304, edge devices 202, or other securitymechanisms, where they may be used to limit risk, as described ingreater detail herein. In certain embodiments, an EBC access module 122may be implemented to provide administrative access to variouscomponents of the EBC system 120, as shown in FIG. 15. In certainembodiments, the EBC access management module 122 may include a userinterface (UI), or a front-end, or both, familiar to skilledpractitioners of the art.

FIGS. 16a and 16b are a simplified block diagram showing referencearchitecture components of an entity behavior catalog (EBC) systemimplemented in accordance with an embodiment of the invention forperforming certain EBC operations. In various embodiments, the EBCsystem 120 may be implemented to generate, manage, store, or somecombination thereof, information related to the behavior of anassociated entity. In certain embodiments, the EBC system 120 may beimplemented to provide an inventory of entity behaviors for use whenperforming a security operation, described in greater detail herein.

In certain embodiments, an entity behavior catalog (EBC) system 120 maybe implemented to identify a security related activity, described ingreater detail herein. In certain embodiments, the security relatedactivity may be based upon an observable, likewise described in greaterdetail herein. In certain embodiments, the observable may include eventinformation corresponding to electronically-observable behavior enactedby an entity. In certain embodiments, the event informationcorresponding to electronically-observable behavior enacted by an entitymay be received from an electronic data source, such as the EBC datasources 810 shown in FIGS. 8, 15, 16 b, and 17.

In certain embodiments, as likewise described in greater detail herein,the EBC system 120 may be implemented to identify a particular event ofanalytic utility by analyzing an observable associated with a particularsecurity related activity. In certain embodiments, the EBC system 120may be implemented to generate entity behavior catalog data based uponan identified event of analytic utility. In certain embodiments, anobservable 906 may be derived, as described in greater detail herein,from an identified event of analytic utility. In various embodiments,the EBC system 120 may be implemented to associate certain entitybehavior data it may generate with a predetermined abstraction level,described in greater detail herein.

In various embodiments, the EBC system 120 may be implemented to usecertain entity behavior catalog data, and an associated abstractionlevel, to generate a hierarchical set of entity behaviors, described ingreater detail herein. In certain embodiments, the hierarchical set ofentity behaviors generated by the EBC system 120 may represent anassociated security risk, likewise described in greater detail herein.Likewise, as described in greater detail herein, the EBC system 120 maybe implemented in certain embodiments to store the hierarchical set ofentity behaviors within a repository of EBC data 690.

In various embodiments, the EBC system 120 may be implemented to receivecertain event information, described in greater detail herein,corresponding to an event associated with an entity interaction,likewise described in greater detail herein. In various embodiments, theevent information may be generated by, received from, or a combinationthereof, certain EBC data sources 810. In certain embodiments, such EBCdata sources 810 may include endpoint devices 304, edge devices 202,identity and access 1604 systems familiar to those of skill in the art,as well as various software and data security 1606 applications. Invarious embodiments, EBC data sources 810 may likewise include outputfrom certain processes 1608, network 1610 access and traffic logs,domain 1612 registrations and associated entities, certain resources750, described in greater detail herein, event logs 1614 of all kinds,and so forth.

In certain embodiments, EBC system 120 operations are begun with thereceipt of information associated with a particular event. In certainembodiments, information associated with the event may include userentity profile attributes, user behavior factors, user entity mindsetfactors, entity state information, and contextual information, describedin greater detail herein, or a combination thereof. In certainembodiments, the event may be associated with a user/device, auser/network, a user/resource, or a user/user interaction, as describedin greater detail herein. In various embodiments, certain user entityprofile data, user entity mindset profile data, non-user entity profiledata, entity state data, contextual information, and temporalinformation stored in the repository of EBC data 690 may be retrievedand then used to perform event enrichment operations to enrich theinformation associated with the event. In certain embodiments, the eventenrichment operations may be performed by the event enrichment 680module.

In various embodiments, an observable 906, described in greater detailherein, may be derived from the resulting enriched, contextualizedevent. As shown in FIG. 16b , examples of such observables may includefirewall file download 1618, data loss protection (DLP) download 1620,and various operating system (OS) events 1622, 1626, and 1634. Aslikewise shown in FIG. 16b , other examples of such observables mayinclude cloud access security broker (CASB) events 1624 and 1632,endpoint spawn 1628, insider threat process start 1630, DLP share 1636,and so forth. In certain embodiments, the resulting observables 906 mayin turn be respectively associated with a corresponding observableabstraction level, described in greater detail herein.

In certain embodiments, security related activity abstractionoperations, described in greater detail herein, may be performed on theresulting observables 906 to generate a corresponding security relatedactivity 908. In various embodiments, a security related activity 908may be expressed in a Subject Action Object format and associated withobservables 906 resulting from event information received from certainEBC data sources 810. In certain embodiments, a security relatedactivity abstraction operation, described in greater detail herein, maybe performed to abstract away EBC data source-specific knowledge anddetails when expressing an entity behavior. For example, rather thanproviding the details associated with a “Windows:4624” non-user entityevent, the security related activity 908 is abstracted to a “User LoginTo Device” OS event 1622, 1626, 1634.

As shown in FIG. 16b , examples of security related activities 908 mayinclude “user downloaded document” 1622, “device spawned process” 1644,“user shared folder” 1646, and so forth. To provide other examples, thesecurity related activity 908 “user downloaded document” 1622 may beassociated with observables 906 firewall file download 1618, DLPdownload 1620, OS event 1622, and CASB event 1624. Likewise, thesecurity related activity 908 “device spawned process” 1644, may beassociated with observables 906, OS event 1626, endpoint spawn 1628, andinsider threat process start 1630. The security related activity 908“user shared folder” 1646 may likewise be associated with observables906 CASB event 1632, OS event 1634, and DLP share 1636.

In certain embodiments, security related activities 908 may in turn berespectively associated with a corresponding security related activityabstraction level, described in greater detail herein. In variousembodiments, activity sessionization operations, likewise described ingreater detail herein, may be performed to respectively associatecertain events and security related activities 908 with correspondingactivity sessions, likewise described in greater detail herein.Likewise, as described in greater detail herein, the resulting sessioninformation may be used in various embodiments to associate certainevents of analytic utility, or their corresponding observables 906, ortheir corresponding security related activities 908, or a combinationthereof, with a particular activity session.

In certain embodiments, the resulting security related activities 908may be processed to generate an associated EBP element 980, as describedin greater detail herein. In various embodiments, the EBP element 980may include user entity attribute 1648 information, non-user entityattribute 1650 information, entity behavior 1652 information, and soforth. In certain of these embodiments, the actual information includedin a particular EBP element 980, the method by which it is selected, andthe method by which it is associated with the EBP element 980, is amatter of design choice. In certain embodiments, the EBP elements 980may in turn be respectively associated with a corresponding EBP elementabstraction level, described in greater detail herein.

In various embodiments, certain EBP elements 980 may in turn beassociated with a particular EBP 638. In certain embodiments, the EBP638 may be implemented as a class of user 1662 EBPs, an entity-specificuser 1662 EBP, a class of non-user 1666 EBPs, an entity-specificnon-user 1668 EBP, and so forth. In certain embodiments, classes of user1662 and non-user 1666 EBPs may respectively be implemented as aprepopulated EBP, described in greater detail herein. In variousembodiments, certain entity data associated with EBP elements 980associated with the classes of user 1662 and non-user 1666 EBPs may beanonymized. In certain embodiments, the EBP 638 may in turn beassociated with an EBP abstraction level, described in greater detailherein.

In certain embodiments, security risk use case association operationsmay be performed to associate an EBP 638 with a particular security riskuse case 1670. As shown in FIG. 16a , examples of such security risk usecases 1670 include “data exfiltration” 1672, “data stockpiling” 1674,“compromised insider” 1676, “malicious user” 1678, and so forth. Invarious embodiments, entity behavior of analytic utility resulting fromthe performance of certain analytic utility detection operations may beused identify one or more security risk use cases 1670 associated with aparticular EBP 638. In certain embodiments, identified security risk usecases may in turn be associated with a security risk use caseabstraction level, described in greater detail herein.

In certain embodiments, the results of the security risk use caseassociation operations may be used to perform security vulnerabilityscenario association operations to associate one or more security riskuse cases 1670 to one or more security vulnerability scenarios 1680,described in greater detail herein. As shown in FIG. 16a , examples ofsecurity vulnerability scenarios 1680 include “accidental disclosure”1682, “account takeover” 1684, “theft of data” 1686, “sabotage” 1688,“regulatory compliance” 1690, “fraud” 1692, “espionage” 1694, and soforth. To continue the example, the “theft of data” 1686 securityvulnerability scenario may be associated with the “data exfiltration”1672, “data stockpiling” 1674, “compromised insider” 1676, “malicioususer” 1678 security risk use cases 1670. Likewise the “sabotage” 1688and “fraud” 1692 security vulnerability scenarios may be respectivelyassociated with some other security risk case 1670. In certainembodiments, the associated security vulnerability scenarios may in turnbe associated with a security vulnerability scenario abstraction level,described in greater detail herein.

FIG. 17 is a simplified block diagram showing the mapping of entitybehaviors to a risk use case scenario implemented in accordance with anembodiment of the invention. In certain embodiments, an entity behaviorcatalog (EBC) system 120 may be implemented, as described in greaterdetail herein, to receive event information from a plurality of EBC datasources 810, which is then processed to determine whether a particularevent is of analytic utility. In certain embodiments, the EBC system 120may be implemented to derive observables 906 from identified events ofanalytic utility, as likewise described in greater detail herein. Incertain embodiments, the EBC system 120 may be implemented, as describedin greater detail herein, to associate related observables 906 with aparticular security related activity 908, which in turn is associatedwith a corresponding security risk use case 1670. In variousembodiments, certain contextual information may be used, as described ingreater detail herein, to determine which security related activities908 may be associated with which security risk use cases 1670.

In certain embodiments, a single 1760 security related activity 908 maybe associated with a particular security risk use case 1670. Forexample, as shown in FIG. 17, event data may be received from aUnix/Linux® event log 1712 and a Windows® directory 1704. In thisexample, certain event data respectively received from the Unix/Linux®event log 1712 and Windows® directory 1704 may be associated with anevent of analytic utility, which results in the derivation ofobservables 906 “File In Log Deleted” 1722 and “Directory Accessed”1724. To continue the example, the resulting observables 906 “File InLog Deleted” 1722 and “Directory Accessed” 1724 may then be associatedwith the security related activity 908 “Event Log Cleared” 1744. Inturn, the security related activity 908 “Event Log Cleared” 1744 may beassociated with security risk use case 1670 “Administrative Evasion”1758.

In certain embodiments, two or more 1764 security related activities 908may be associated with a particular security risk use case 1670. Forexample, as shown in FIG. 17, event data may be received from anoperating system (OS) 1706, an insider threat 1708 detection system, anendpoint 1710 and a firewall 1712. In this example, certain event datarespectively received from the operating system (OS) 1706, an insiderthreat 1708 detection system, an endpoint 1710 and a firewall 1712 maybe associated with an event of analytic utility. Accordingly,observables 906 “Security Event ID” 1726, “New Connection” 1728, may berespectively derived from the event data of analytical utility receivedfrom the endpoint 1710 and the firewall 1712 EBC data sources 810.Likewise, observables 906 “Connection Established” 1730 and “NetworkScan” 1732 may be respectively derived from the event data of analyticalutility received from the OS 1706, the insider threat 1708 detectionsystem, EBC data sources 810.

To continue the example, the resulting observables 906 “Security EventID” 1726, “New Connection” 1728 and “Connection Established” 1730 may beassociated with security related activity 908 “Device Connected To Port”1746. Likewise, observable 906 “Network Scan” 1732 may be associatedwith security related activity 908 “Network Scan” 1748. In turn, thesecurity related activities 908 “Device Connected To Port” 1746 and“Network Scan” 1732 may be associated with security risk use case 1670“Internal Horizontal Scanning” 1762.

In certain embodiments, a complex set 1768 of security relatedactivities 908 may be associated with a particular security risk usecase 1670. For example, as shown in FIG. 17, event data may be receivedfrom an OS 1714, an internal cloud access security broker (CASB) 1716,an external CASB 1718, and an endpoint 1720. In this example, certainevent data respectively received from the OS 1714, the internal cloudaccess security broker (CASB) 1716, the external CASB 1718, and theendpoint 1720 may be associated with an event of analytic utility.

Accordingly, observables 906 “OS Event” 1734, “CASB Event” 1740, and“New Application” 1742 may be respectively derived from the event dataof analytical utility provided by the OS 1714, the external CASB 1718,and the endpoint 1720 EBC data sources 810. Likewise, a first “CASBEvent ID” 1736 observable 906 and a second “CASB Event ID” 1738observable 906 may both be derived from the event data of analyticalutility received from the internal CASB 1716 EBC data source 810

To continue the example, the “OS Event” 1734, the first “CASB Event ID”1736, and “New Application” 1742 observables 906 may then berespectively associated with security related activities 908 “New USBDevice” 1750, “Private Shareable Link” 1752, and “File TransferApplication” 1756. Likewise, second “CASB Event ID” 1738 observable 906and the “CASB Event” 1740 observable 906 may then be associated withsecurity related activity 908 “Public Shareable Link” 1754. In turn, thesecurity related activities 908 “New USB Device” 1750, “PrivateShareable Link” 1752, “Public Shareable Link” 1754, and “File TransferApplication” 1756 may be associated with security risk use case 1670“Data Exfiltration Preparations” 1766.

FIG. 18 is a simplified block diagram of an entity behavior catalog(EBC) system environment implemented in accordance with an embodiment ofthe invention. In certain embodiments, the EBC system environment may beimplemented to detect user or non-user entity behavior of analyticutility and respond to mitigate risk, as described in greater detailherein. In certain embodiments, the EBC system environment may beimplemented to include a security analytics system 118, likewisedescribed in greater detail herein. In certain embodiments, the securityanalytics system 118 may be implemented to include an EBC system 120.

In certain embodiments, the EBC system 120, as described in greaterdetail herein, may be implemented to use entity behavior information togenerate an entity behavior profile (EBP), likewise as described ingreater detail herein. In certain embodiments, the security analyticssystem 118 may be implemented to use one or more session-basedfingerprints to perform security analytics operations to detect suchuser or non-user entity behavior. In certain embodiments, the securityanalytics system 118 may be implemented to monitor entity behaviorassociated with a user entity, such as a user ‘A’ 702 or user ‘B’ 772.In certain embodiments, the user or non-user entity behavior may bemonitored during user/device 730, user/network 742, user/resource 748,and user/user 770 interactions. In certain embodiments, the user/user770 interactions may occur between a first user, such as user ‘A’ 702and user ‘B’ 772.

In certain embodiments, as described in greater detail herein, anendpoint agent 306 may be implemented on an endpoint device 304 toperform user or non-user entity behavior monitoring. In certainembodiments, the user or non-user entity behavior may be monitored bythe endpoint agent 306 during user/device 730 interactions between auser entity, such as user ‘A’ 702, and an endpoint device 304. Incertain embodiments, the user or non-user entity behavior may bemonitored by the endpoint agent 306 during user/network 742 interactionsbetween user ‘A’ 702 and a network, such as an internal 744 or external746 network. In certain embodiments, the user or non-user entitybehavior may be monitored by the endpoint agent 306 during user/resource748 interactions between user ‘A’ 702 and a resource 750, such as afacility, printer, surveillance camera, system, datastore, service, andso forth. In certain embodiments, the monitoring of user or non-userentity behavior by the endpoint agent 306 may include the monitoring ofelectronically-observable actions respectively enacted by a particularuser or non-user entity. In certain embodiments, the endpoint agent 306may be implemented in combination with the security analytics system 118and the EBC system 120 to detect entity behavior of analytic utility andperform a security operation to mitigate risk.

In certain embodiments, the endpoint agent 306 may be implemented toinclude an event analytics 310 module and an EBP feature pack 1808. Incertain embodiments, the EBP feature pack 1808 may be furtherimplemented to include an event data detector 1810 module, an entitybehavior data detector 1812 module, an event and entity behavior datacollection 1814 module, an analytic utility detection 1816 module, anobservable derivation 1818 module, and a security related activityabstraction 1820 module, or a combination thereof. In certainembodiments, the event data detector 1810 module may be implemented todetect event data, described in greater detail herein, resulting fromuser/device 730, user/network 742, user/resource 748, and user/user 770interactions. In various embodiments, the entity behavior detector 1812module may be implemented to detect certain user and non-user entitybehaviors, described in greater detail herein, resulting fromuser/device 730, user/network 742, user/resource 748, and user/user 770interactions.

In various embodiments, the event and entity behavior data collection1814 module may be implemented to collect certain event and entitybehavior data associated with the user/device 730, user/network 742,user/resource 748, and user/user 770 interactions. In certainembodiments, the analytic utility detection 1816 may be implemented todetect entity behavior of analytic utility associated with eventscorresponding to the user/device 730, user/network 742, user/resource748, and user/user 770 interactions. In various embodiments, theobservable derivation 1818 module may be implemented to deriveobservables, described in greater detail herein, associated with eventsof analytical utility corresponding to the user/device 730, user/network742, user/resource 748, and user/user 770 interactions. In variousembodiments, the security related activity abstraction 1820 module maybe implemented to generate a security related activity, likewisedescribed in greater detail herein, from the observables derived by theobservable derivation 1816 module.

In certain embodiments, the endpoint agent 306 may be implemented tocommunicate the event and entity behavior collected by the event andentity behavior data collector 1814 module, the observables derived bythe observable derivation 1816 module, and the security relatedactivities generated by the security related activity abstraction 1818,or a combination thereof, to the security analytics 118 system. Incertain embodiments, the security analytics system 118 may beimplemented to receive the event and entity behavior data, theobservables, and the security related activities provided by theendpoint agent 306. In certain embodiments, the endpoint agent 306 maybe implemented to provide the event and entity behavior data, theobservables, and the security related activities, or a combinationthereof, to the security analytics system 118. In turn, in certainembodiments, the security analytics system 118 may be implemented incertain embodiments to provide the event and entity behavior data, theobservables, and the security related activities, or a combinationthereof, to the EBC system 120 for processing.

In certain embodiment, the EBC system 120 may be implemented to includean entity behavior contextualization 1880 module, an EBP sessiongenerator 1882 module, an EBP element generator 1884, or a combinationthereof. In certain embodiments, the EBP element generator 1882 modulemay be implemented to process the event and entity behavior data, theobservables, and the security related activities provided by theendpoint agent 306 to generate EBP elements, described in greater detailherein. In certain embodiments, the EBP session generator 1884 may beimplemented to use the event and entity behavior data, the observables,and the security related activities provided by the endpoint agent 306,to generate session information. In certain embodiments, the EBP sessiongenerator 1884 may be implemented to use the resulting sessioninformation to generate an activity session, described in greater detailherein. In various embodiments, as likewise described in greater detailherein, certain EBP management operations may be performed to associateEBP elements generated by the EBP element generator 1882 module with acorresponding EBP. Likewise, certain EBP management operations may beperformed to use the session information generated by the EBP sessiongenerator 1884 module to associate a particular EBP element with aparticular EBP

In certain embodiments, the EBC system 120 may be implemented as adistributed system. Accordingly, various embodiments of the inventionreflect an appreciation that certain modules, or associatedfunctionalities, may be implemented either within the EBC system 120itself, the EBP feature pack 1808, an edge device 202, an internal 744or external 746 network, an external system 780, or some combinationthereof. As an example, the functionality provided, and operationsperformed, by the analytic utility detection 1816, observable derivation1818 and security related activity abstraction 1820 modules may beimplemented within the EBC system 120 in certain embodiments. Likewise,the functionality provided, and operations performed, by the entitybehavior contextualization 1880, EBP session generator 1882, and EBPelement generator 1884 may be implemented within the EBP feature pack1808. Those of skill in the art will recognize that many suchimplementations are possible. Accordingly, the foregoing is not intendedto limit the spirit, scope, or intent of the invention.

FIG. 19 shows a human-centric risk modeling framework implemented inaccordance with an embodiment of the invention. As used herein,human-centric risk broadly refers to any risk associated with theenactment of a behavior by a user entity, as described in greater detailherein. In certain embodiments, the human-centric risk modelingframework 1900 shown in FIG. 19 may be used by a human-centric riskmodeling system, described in greater detail herein, to perform asecurity analytics operation, likewise described in greater detailherein. In certain embodiments, the security analytics operation may beperformed independently by the human-centric risk modeling system or incombination with a security analytics system. Various embodiments of theinvention reflect an appreciation that known approaches to human-centricrisk modeling have certain limitations that often pose challenges forsecurity-related implementation. Likewise, various embodiments of theinvention reflect an appreciation that implementation of thebehavior-based risk modeling framework 1900 may assist in addressingcertain of these limitations.

In certain embodiments, behavior enacted by a user entity may be ofanalytic utility, likewise described in greater detail herein. Incertain embodiments, such entity behavior of analytic utility may bedetermined to be anomalous, abnormal, unexpected, malicious, or somecombination thereof, as described in greater detail herein. In certainembodiments, the human-centric risk modeling framework 1900 may beimplemented as a reference model for assessing the risk associated witha user entity enacting anomalous, abnormal, unexpected, or maliciousbehavior, or some combination thereof. In certain embodiments, theassessment of such risk may be qualitative, quantitative, or acombination of the two. In certain embodiments, the risk assessment maybe quantitatively expressed in the form of a user entity risk score1930, described in greater detail herein.

In certain embodiments, an observable 906, described in greater detailherein, may be derived from an associated event, as likewise describedin greater detail herein. In certain embodiments, one or moreobservables 906 may be processed to generate a corresponding securityrelated activity 908. In various embodiments, the security relatedactivities 908 may be associated with the enactment of certain userentity behaviors 1902, described in greater detail herein. In variousembodiments, as described in greater detail herein, certain user entitybehaviors 1902 may be enacted during one or more activity sessions 910,likewise described in greater detail herein. In certain embodiments, oneor more security related activities 908 may be processed to generate acorresponding activity session 910. In certain embodiments, the activitysession 910 may be processed, as described in greater detail herein, togenerate a corresponding session fingerprint 912.

In certain embodiments, as described in greater detail herein, theresulting session fingerprints 912 may be associated with acorresponding entity behavior profile (EBP) 638, which in turn may beassociated with a particular security risk use case 1904. In certainembodiments, the security risk use case 1904 may in turn be associatedwith a particular phase 1906 of an outcome-oriented kill chain,described in the descriptive text associated with FIG. 23. In certainembodiments, as likewise described in the descriptive text associatedwith FIG. 23, a different security risk persona 1908 may be associatedwith each phase 1906 of an outcome-oriented kill chain. In certainembodiments, the security risk use case 1904 may be associated asingle-phase kill chain, as described in the descriptive text associatedwith FIG. 24.

In certain embodiments, as described in the descriptive text associatedwith FIG. 23, each phase 1906 of an outcome-oriented kill chain may beimplemented to have a corresponding security risk persona 1908. As usedherein, a security risk persona 1908 broadly refers to a descriptorcharacterizing the behavioral pattern exhibited by a user entity duringthe enactment of certain user entity behaviors 1902 associated with aparticular phase 1906 of a kill chain. In certain embodiments, thesecurity risk persona 1908 may directly or indirectly characterize, orotherwise reference, one or more user entity behaviors 1902. As anexample, a user entity may exhibit user entity behaviors 1902 typicallyassociated with data stockpiling. In this example, the security riskpersona 1908 for the user entity might be “Data Stockpiler,” or“Stockpiler.” In certain embodiments, as described in the descriptivetext associated with FIG. 24, a security risk persona 1908 may beimplemented as a contextual risk persona 2412, such as “Leaver.”

In various embodiments, certain user entity behaviors 1902 associatedwith a security risk use case 1904 may be used to define, or otherwisedescribe or characterize, a particular security risk persona 1908. Inthese embodiments, the user entity behaviors 1902 selected for use, andthe method by which the security risk persona 1908 is defined, orotherwise described or characterized, is a matter of design choice. Inthese embodiments, the descriptor selected to characterize a particularbehavioral pattern exhibited by a user entity is likewise a matter ofdesign choice. In certain embodiments, a security risk persona 1908 mayhave a corresponding persona baseline risk score 1910. In certainembodiments, the persona baseline risk score 1910 may be implemented toreflect a quantitative assessment of the risk corresponding to the userentity behaviors 1902 referenced by its associated security risk persona1908.

In various embodiments, as described in greater detail herein, certainsecurity risk personas 1908 may be implemented to track dynamic changesin user entity behaviors 1902. In various embodiments, the ability totrack dynamic changes in user entity behaviors 1902 may facilitate theidentification and management of rapidly developing threats.Accordingly, certain embodiments of the invention reflect anappreciation that such tracking may be indicative of dynamic behavioralchange associated with a particular user entity. Likewise, certainembodiments of the invention reflect an appreciation that security riskpersonas 1908 may shift, or transition, from one to another, and indoing so, reflect important shifts in behavior that are relevant tounderstanding an entity's traversal of kill chains.

In certain embodiments, a security risk persona 1908 may be used todetermine a particular user entity's predisposition 1912. As usedherein, a user entity predisposition 1912 broadly refers to an enduser's enduring behavioral trends. In various embodiments, a user entitypredisposition 1912 may be implemented to represent a user entity'sdominant or enduring security risk persona 1908 over a particular periodof time. In certain of these embodiments, establishing a user entity'spredisposition 1912 may require tracking their associated security riskpersonas 1908 over such a period of time. In certain embodiments, a userentity's predisposition 1908 may be established over a plurality ofactivity sessions 910.

In certain embodiments, a user entity's predisposition 1912 may includeone or more characteristics historically or otherwise linked to apropensity to exhibit malicious behavior (e.g., low honesty-humility,manipulative, narcissism, etc.). Certain embodiments of the inventionreflect an appreciation that a user entity's predisposition 1912 mayprove useful for identifying and managing slower moving or ongoingthreats, such as low and slow data exfiltration or espionage.Accordingly, certain embodiments of the invention reflect anappreciation that user entity's predispositions 1912, as typicallyimplemented, may create an avenue for understanding a user entity'sbehaviors 1902 within the context of certain kill chain phases 1906 in alonger-term capacity.

Likewise, certain embodiments of the invention reflect an appreciationthat understanding user entity predispositions 1912 may assist indetermining a user entity's potential alignment with kill chain phases1906 and associated security vulnerability scenarios 1914, described ingreater detail herein. Certain embodiments of the invention likewisereflect an appreciation that user entity predispositions 1912 may beused to create meaningful profiles of a user entity's behavior 1902patterns, such as an EBP 638. Accordingly, certain embodiments of theinvention reflect an appreciation that user entity predispositions 1912may likewise be used to decrease false positives, maintain a meaningfulrisk score for the user entity during periods of absence, and toidentify subtle yet dangerous changes in behavior over a longertimeline.

In various embodiments, the human-centric risk model 1900 may bereferenced, or otherwise used, in the performance of security riskcalculation operations to provide qualitative and quantitativeassessments of security risk associated with certain user entitybehaviors 1902. In certain of these embodiments, these security riskcalculation operations may be based upon concerning behaviors 1916associated with a particular user entity. As used herein, a concerningbehavior 1916 broadly refers to a user entity behavior 1902 of analyticutility, described in greater detail herein, that may be considered apotential security risk. In certain embodiments, as likewise describedin greater detail herein, such entity behavior 1902 of analytic utilitymay be determined to be anomalous, abnormal, unexpected, malicious, orsome combination thereof. In these embodiments, the user entitybehaviors 1902 determined to be a concerning behavior 1916, and themethod by which the determination is performed, is a matter of designchoice.

In certain embodiments, a particular concerning behavior 1916 may have acorresponding concerning behavior risk score 1918. In certainembodiments, the concerning behavior risk score 1918 may be implementedas a numeric value. In certain embodiments, the numeric value of theconcerning behavior risk score 1918 may be implemented to quantitativelyreflect the security risk associated with a particular concerningbehavior 1916.

In various embodiments, the concerning behavior risk score 1918 may beimplemented to be within a certain range of numeric values. In certainembodiments, the range of numeric values may be implemented to have alower and upper bound. As an example, the concerning behavior risk score1918 for a particular concerning behavior 1916 may be implemented tohave a lower bound of ‘0,’ indicating extremely low risk, and an upperbound ‘100,’ indicating extremely high risk. To illustrate the example,a user entity may exhibit a concerning behavior 1916 by accessing acorporate server containing sales data, which in turn may result in thegeneration of an associated concerning behavior risk score 1918 of ‘50.’In this example, the concerning behavior risk score 1918 of ‘50’ mayindicate moderate risk. However, downloading the same sales data to alaptop computer may result in the generation of a concerning behaviorrisk score of ‘75,’ indicating high risk. To further illustrate theexample, downloading the same sales data to a laptop computer, and thenstoring the sales data on a flash drive, may result in the generation ofa concerning behavior risk score of ‘90,’ indicating very high risk.

In certain embodiments, the numeric value of a concerning behavior riskscore 1918, or the upper and lower bounds of its associated range ofnumeric values, or both, may be implemented to be configurable. Incertain embodiments, the selection of a numeric value for a particularconcerning behavior risk score 1918, or the selection of the upper andlower bounds of its associated range of numeric values, or both, may beperformed manually, automatically, or a combination thereof. In theseembodiments, the numeric value selected for a particular concerningbehavior risk score 1918, or the upper and lower bounds selected for itsassociated range of numeric values, or both, is a matter of designchoice.

Certain embodiments of the invention reflect an appreciation that notall user entity behaviors 1902 are concerning behaviors 1916. As anexample, a user entity behavior 1902 such as uploading a file to a cloudapplication is something many users do over the course of a day and maynot expose an organization to additional risk or indicate that the useris engaging in particularly risky behavior. However, uploading a file toa cloud application may be considered a concerning behavior 1916,dependent upon the presence of certain user entity attributes (e.g., jobtitle, physical location, working hours, etc.), and the behavioralcontext associated with the user entity.

As another example, two user entities may be observed to be performingthe user entity behavior 1902 of logging into a software coderepository. In this example, the first user has the job title of SeniorEngineering Director, they are accessing the repository during normalworking hours from their assigned corporate laptop, and have accessedthis particular repository multiple times before. Accordingly, allassociated user entity behaviors 1902 appear normal. However, the seconduser entity, has the job title of Junior Marketing Administrator, theyare accessing the repository during a weekend from their corporatelaptop machine, but from a public Internet Protocol (IP) address, andhave never attempted to access this particular repository before.Furthermore, they have attempted to access the repository eleven timeswithin a thirty minute time interval.

Consequently, while the user entity behavior 1902 of logging into thecode repository may be the same for both user entities, the user entitybehavior 1902 of the second user entity is considered a concerningbehavior 1916 due to the additional context of their associated userentity attributes. As a result, the user entity risk score 1930associated with the second user entity may be adjusted accordingly.Various embodiments of the invention reflect an appreciation thatcertain user entity behaviors 1902 may be considered so high-risk thatno associated user entity attributes are necessary to establish them asconcerning behaviors 1916. Certain embodiments of the invention furtherreflect an appreciation that such user entity behaviors 1902 maytypically be associated with traditional threat-centric cybersecurityattacks where the severity of associated security related activities iswell-defined.

Certain embodiments of the invention reflect an appreciation thatconcerning behaviors 1916 are primarily oriented towards current, orshort-term, user entity behaviors 1902. Accordingly, concerningbehaviors 1916 may be used in certain embodiments to more fullyunderstand user entity behaviors 1902 in the context of each phase 1906of an outcome-oriented kill chain, or to identify user entity behavior1902 patterns of interest in a single-phase 1906 kill chain. Certainembodiments of the invention likewise reflect an appreciation that suchcontext can be used advantageously to decrease false positives andimprove the accuracy of user entity risk scores 1930.

In certain embodiments, a contextual modifier 1920 may be implemented toadjust the value of a concerning risk score 1918 for an associatedconcerning behavior 1916. As used herein, a contextual modifier 1920broadly refers to a circumstance, aspect, dynamic, attribute, or otherconsideration used to clarify, mitigate, exacerbate, or otherwise affectthe perception, meaning, understanding, or assessment of a security riskassociated with a particular concerning behavior 1916. In certainembodiments, a contextual modifier 1920 may be implemented as a numericvalue.

In certain embodiments, the numeric value of a contextual modifier 1920may be implemented to quantitatively reflect its effect on an associatedconcerning behavior risk score 1918. In certain embodiments, the numericvalue of a contextual modifier 1920 may be implemented to adjust thenumeric value of an associated concerning risk score 1918 tocontextually quantify the security risk associated with a particularconcerning behavior 1916. In certain embodiments the numeric value of aparticular contextual modifier 1920, individually or in combination withanother contextual modifier 1920, may be implemented as a security riskweighting factor. In certain embodiments, the weighting factor may beapplied to a concerning behavior risk score 1918 to contextually reflectthe security risk associated with an associated concerning behavior1916.

In certain embodiments, the numeric value of a particular contextualmodifier 1920 may be implemented to be configurable. In variousembodiments, the numeric value of a contextual modifier 1920 may beimplemented to be within a certain range of numeric values. In certainembodiments, the range of numeric values may be implemented to have alower and upper bound. In certain embodiments, the selection of anumeric value for a particular contextual modifier 1920, or theselection of the upper and lower bounds of its associated range ofnumeric values, or both, may be performed manually, automatically, or acombination thereof. In these embodiments, the numeric value selectedfor a particular contextual modifier 1920, or the upper and lower boundsselected for its associated range of numeric values, or both, is amatter of design choice.

In certain embodiments, the numeric value of one or more contextualmodifiers 1920 may be applied as a weighting factor to a concerningbehavior risk score 1918 associated with a particular concerningbehavior 1916 to generate a user entity risk score 1930, as described ingreater detail herein. In various embodiments, the resulting user entityrisk score 1930 may be used to contextually quantify the security risk1928 associated with a particular user entity exhibiting one or moreconcerning behaviors 1916.

In certain embodiments, a contextual modifier 1920 may be categorized asbeing a stressor 1922, organizational 1924, or motivation 1926contextual modifier 1920. As used herein, a stressor 1922 contextualmodifier 1920 broadly refers to any issue that may influence orotherwise affect a user entity's behavior 1902. In certain embodiments,a particular stressor 1922 contextual modifier 1920 may be classified aspersonal, professional, financial, legal, and so forth. In certainembodiments, one or more stressor 1922 contextual modifier 1920 classesmay be associated with a particular security risk use case 1904 orconcerning behavior 1916. In certain embodiments, stressor 1922contextual modifiers 1920 are not used in isolation. Instead they arelinked to an associated concerning behavior.

As used herein, an organizational stressor 1924 contextual modifier 1920broadly refers to any event that occurs within an organization, or largegroup, that may influence or otherwise affect a user entity's behavior1902 during a particular interval of time. For example, organizationalstressor 1924 contextual modifiers 1920 may include organizationalrestructuring, layoffs, and geographical shifts, all of which can createa sense of uncertainty within an organization. To continue the example,certain of these organizational stressor 1924 contextual modifiers 1920may prompt an employee to seek other employment opportunities.Furthermore, it is common for employees to feel they have personalownership of their work. As a result, it is common for an employee tocollect and exfiltrate intellectual property when changing jobs.Accordingly, certain embodiments of the invention reflect anappreciation that the use of organizational stressor 1924 contextualmodifiers 1920 may assist in identifying concerning behaviors 1916associated with high employee turnover, loss of intellectual property,and other organization vulnerabilities.

As used herein, a motivation 1926 contextual modifier 1920 broadlyrefers to one or more user entity behaviors 1902 that may provide anindication of a user entity's motivation for enacting that user behavior1902. In certain embodiments, a motivation 1926 contextual modifier 1920may be associated with a particular phase 1906 of an outcome-orientedkill chain, such as a data theft kill chain. In certain embodiments,concerning behaviors 1916 may be used, with or without contextualmodifiers 1920, individually or in combination, with security riskpersonas 1908 and user entity predispositions 1912 to enablerisk-adaptive enforcement of security policies.

As described in greater detail herein, an EBP 638 may be implemented invarious embodiments to collect certain user entity behavior 1902 andother information associated with a particular user entity. Examples ofsuch information may include user entity attributes, such as their name,position, tenure, office location, working hours, and so forth. Incertain embodiments, the EBP 638 may likewise be implemented to collectadditional behavioral information associated with the human-centric riskmodel 1900. Examples of such additional information may include certainhistorical information associated with kill chain phases 1906,associated security risk personas 1908, user entity predisposition 1912,security vulnerability scenarios 1914, concerning behaviors 1916, anduser entity risk scores 1930.

As an example, an EBP 638 may contain the following user entityinformation:

-   -   Entity Name: John Smith    -   Entity Risk Score: 50%    -   Current Security Risk Persona: Wanderer    -   Security Vulnerability Scenarios: Data Theft (Medium Confidence)        -   Fraud (Low Confidence)    -   Leave Risk Score 55%

FIG. 20 is a graphical representation of an ontology showing examplestressor contextual modifiers implemented in accordance with anembodiment of the invention. As described in greater detail herein, astressor 1922 contextual modifier may trigger a concerning behavior. Aslikewise described in greater detail herein, stressor 1922 contextualmodifiers may be implemented in certain embodiments to providemeaningful context for interpreting or influencing concerning behaviorrisk scores. In certain embodiments, classes of stressor 1922 contextualmodifiers may include personal 2004, 2006, financial 2008, and legal2010.

Certain embodiments of the invention reflect an appreciation that thepresence of one or more stressor 1922 contextual modifiers willnecessarily trigger a concerning behaviors or other negative activities.However, certain embodiments of the invention likewise reflect anappreciation that user entities engaging in both intentionally maliciousand accidentally risky behaviors are frequently enduring personal 2004,2006, financial 2008, and legal 2010 stress.

As shown in FIG. 20, examples of personal 2004 stressor 1922 contextualmodifiers may include certain life changes, such as separation ordivorce, marriage, the birth, death, or sickness of a family member orfriend, health issues or injuries, pregnancy or adoption, and so forth.Likewise, examples of professional 2006 stressor 1922 contextualmodifiers may include termination or unsatisfactory performance reviews,retirement, business unit reorganization, changes in responsibility orcompensation, co-worker friction, changes in work hours or location, andso forth.

Examples of financial 2008 stressor 1922 contextual modifiers maylikewise include bankruptcy, foreclosure, credit issues, gamblingaddition, and so forth. Likewise, examples of legal 2010 stressor 1922contextual modifiers may include previous arrests, current arrests orincarceration, drug or driving under the influence (DUI) offenses, wagegarnishment, and so forth. Skilled practitioners of the art willrecognize that other 2012 classes of stressor 1922 contextual modifiersare possible. Accordingly, the foregoing is not intended to limit thespirit, scope, or intent of the invention.

FIG. 21 shows a mapping of data sources to stressor contextual modifiersimplemented in accordance with an embodiment of the invention. Invarious embodiments, individual stressor 1922 contextual modifiers maybe implemented to receive input data from certain data sources. Incertain embodiments, these data sources may include communicationchannels 2112 of various kinds, web activity 2114, automated emails2116, human resources 2118 communications, credit reports 2120, andbackground checks 2122. Skilled practitioners of the art will recognizethat individual stressor 1922 contextual modifiers may be implemented toreceive input data from other data sources as well. Accordingly, theforegoing is not intended to limit the spirit, scope, or intent of theinvention.

In certain embodiments, communication channels 2112 may include emails,chat (e.g., Slack®), phone conversations (e.g., telephone, Skype®,etc.), and so forth. In various embodiments, natural language processing(NLP) approaches familiar to those of skill in the art may beimplemented to identify certain stressors within a particularcommunication channel 2112 exchange. In various embodiments, webactivity 2114 may be monitored and processed to identify certainstressors. In various embodiments, web activity 2114 may likewise bemonitored and processed to identify certain web-related data fields,such as search terms, time stamps, domain classification, and domainrisk class. In certain embodiments, auto-generated emails, especiallyfrom Human Capital Management (HCM) or Human Resource (HR) systems maybe implemented to assist organization identify and understand stressorsrelated to a user entity's professional and life events. Likewise, datareceived for human resources 2118, credit reports 2120, and backgroundchecks 2122 may be implemented in certain embodiments, to assist inidentifying and understanding additional stressors related to aparticular user entity.

In certain embodiments, more than one data source may provide input datato a particular stressor 1922 contextual modifier. For example, as shownin FIG. 21, a professional 2006 stressor 1922 contextual modifier may beimplemented to receive input data from certain communication channels2112, web activity 2114, automated emails 2116, and human resources 2118communications. Those of skill in the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 22 is a graphical representation of an ontology showing exampleorganizational contextual modifiers implemented in accordance with anembodiment of the invention. As described in greater detail herein, anorganizational 1924 contextual modifier may trigger a concerningbehavior. As likewise described in greater detail herein, organizational1924 contextual modifiers may be implemented in certain embodiments toprovide meaningful context for interpreting or influencing concerningbehavior risk scores. In certain embodiments, classes of organizational1924 contextual modifiers may include security practices 2204,communication issues 2206, management systems 2208, and work planningand control 2210.

As shown in FIG. 22, examples of security practices 2204 organizational1924 contextual modifiers include hiring practices, security trainingand controls, policy clarity, monitoring and tracking practices, and soforth. Likewise, examples of communication issues 2206 organizational1924 contextual modifiers include inadequate procedures, poorcommunications, and so forth. Examples of management systems 2208organizational 1924 contextual modifiers may likewise includedistractions of various kinds, a non-productive environment,insufficient resources, and lack of career advancement. Other examplesof management systems 2208 organizational 1924 contextual modifiers mayinclude poor management systems, job instability, poor work conditions,and so forth.

Likewise, examples of work planning and control 2210 organizational 1924contextual modifiers include job pressures of different sorts, workload,time factors, work role, task difficulty, lack of autonomy and power,change in routine, lack of breaks, and so forth. Skilled practitionersof the art will recognize that other 2212 classes of organizational 1924contextual modifiers are possible. Accordingly, the foregoing is notintended to limit the spirit, scope, or intent of the invention.

FIG. 23 shows security risk persona transitions associated with acorresponding outcome-oriented kill chain implemented in accordance withan embodiment of the invention. In certain embodiments, a securityanalytics system, an entity behavior catalog (EBC) system, or ahuman-centric risk modeling system, or a combination thereof, may beimplemented to monitor the behavior of a particular user entity, asdescribed in greater detail herein. In certain embodiments, suchmonitoring may include observing an electronically-observable datasource, such as the EBC data sources shown in FIGS. 6, 8, 15, 16 b and17.

In certain embodiments, an observable, described in greater detailherein, may be derived from the electronically-observable data source.In certain embodiments, the observable is associated with an event ofanalytic utility, likewise described in greater detail herein. Incertain embodiments, one or more derived observables may then beassociated with a security related activity, as described in greaterdetail herein. In various embodiments, a particular security activitymay be associated with a component of a cyber kill chain.

Skilled practitioners of the art will be familiar with a kill chain,which was originally used as a military concept related to the structureof an attack. In general, the phases of a military kill chain consistedof target identification, force dispatch to target, decision and orderto attack the target, and destruction of the target. Conversely,breaking or disrupting an opponent's kill chain is a method of defenseor preemptive action. Those of skill in the art will likewise befamiliar with a cyber kill chain, developed by the Lockheed Martincompany of Bethesda, Md., which is an adaptation of the military killchain concept that is commonly used to trace the phases of acyberattack.

In certain embodiments, a cyber kill chain may be implemented torepresent multi-stage, outcome-oriented user entity behaviors. Ingeneral, such outcome-oriented cyber kill chains will have at least twodefined phases. However, a single-phase cyber kill chain may beimplemented in certain embodiments, as described in greater detailherein, to represent context-oriented security vulnerability scenarios1914. In certain embodiments, the phases of an outcome-oriented cyberkill chain may be adapted, or otherwise implemented, for a particulartype of cyberattack, such as data theft. For example, as shown in FIG.23, the phases of a data theft kill chain 2340 may include datareconnaissance 2350, data access 2352, data collection 2354, datastockpiling 2356, and data exfiltration 2358.

However, the cyber kill chain concept is not limited to data theft,which relates to theft of an organization's intellectual property. Itcan also be implemented to facilitate the anticipation and recognitionof insider threats, such as insider sabotage, which includes any act byan insider to direct any kind of harm to an organization or its assets.Other insider threats include insider fraud, which relates tomodification, deletion, or theft of an organization's data for personalgain, typically associated with the perpetration of an identity crime(e.g., identity theft, credit card fraud, etc.).

Yet other insider threats include unintentional insider threat, whichincludes any act, or failure to act, by an insider without maliciousintent, that causes harm or substantially increases the probability offuture harm to an organization or its assets. The cyber kill chainconcept can likewise be implemented to address the occurrence, orpossibility thereof, of workplace violence, which relates to any threatof physical violence, harassment, intimidation, or other threateningdisruptive behavior in the workplace. Likewise, the cyber kill chainconcept can be applied to social engineering, advanced ransomware, andinnovative cyberattacks as they evolve.

In certain embodiments, a cyber kill chain may be implemented toanticipate, recognize, and respond to entity behavior of analyticutility that may be determined to be anomalous, abnormal, unexpected,malicious, or some combination thereof, as described in greater detailherein. In certain embodiments, the response to recognition of a killchain may be to perform an associated security operation, likewisedescribed in greater detail herein. In certain embodiments, theperformance of the security operation may result in disrupting orotherwise interfering with the performance, or execution, of one or morecomponents, steps, or phases of a cyber kill chain by affecting theperformance, or enactment, of the security related activity by itsassociated entity.

In certain embodiments, a cyber kill chain may consist of morecomponents, steps, or phases of the data theft kill chain 2340 shown inFIG. 23. For example, in certain embodiments, the cyber kill chain maylikewise include intrusion, exploitation, privilege escalation, lateralmovement, obfuscation/anti-forensics, and denial of service (DoS). Insuch embodiments, the data reconnaissance component 2350 may be executedas an observation stage to identify targets, as well as possible tacticsfor the attack. In certain embodiments, the data reconnaissancecomponent 2350 may not be limited to data exfiltration. For example, itmay be related to other anomalous, abnormal, unexpected, maliciousactivity, such as identity theft.

In certain embodiments, the data access 2352 component may not belimited to gaining access to data. In certain embodiments, the dataaccess 2352 component of a cyber kill chain may be executed as anintrusion phase. In such embodiments, the attacker may use what waslearned in execution of the data reconnaissance 2350 component todetermine how to gain access to certain systems, possibly through theuse of malware or exploitation of various security vulnerabilities. Incertain embodiments, a cyber kill chain may likewise include anexploitation component, which may include various actions and efforts todeliver malicious code and exploit vulnerabilities in order to gain abetter foothold with a system, network, or other environment.

In certain embodiments, a cyber kill chain may likewise include aprivilege escalation component, which may include various actions andefforts to escalate the attacker's privileges in order to gain access tomore data and yet more permissions. In various embodiments, a cyber killchain may likewise include a lateral movement component, which mayinclude moving laterally to other systems and accounts to gain greaterleverage. In certain of these embodiments, the leverage may includegaining access to higher-level permissions, additional data, or broaderaccess to other systems.

In certain embodiments, a cyber kill chain may likewise include anobfuscation/anti-forensics component, which may include various actionsand efforts used by the attacker to hide or disguise their activities.Known obfuscation/anti-forensics approaches include laying false trails,compromising data, and clearing logs to confuse or slow down securityforensics teams. In certain embodiments, the data collection 2254 of acyber kill chain may the collection of data with the intent ofeventually being able to exfiltrate it. In certain embodiments,collected data may be accumulated during a data stockpiling 2356component of a cyber kill chain.

In certain embodiments, a cyber kill chain may likewise include a denialof service (DoS) component, which may include various actions andefforts on the part of an attacker to disrupt normal access for usersand systems. In certain embodiments, such disruption may be performed tostop a cyberattack from being detected, monitored, tracked, or blocked.In certain embodiments, the data exfiltration 2358 component of a cyberkill chain may include various actions and efforts to get data out of acompromised system.

In certain embodiments, information associated with the execution of aparticular phase of a cyber kill chain may be associated with acorresponding security vulnerability scenario 1914, described in greaterdetail herein. In certain embodiments, one of more components of aparticular cyber kill chain may be associated with one or morecorresponding security related use cases, likewise described in greaterdetail herein. In certain embodiments, performance or execution of acomponent or phase of a cyber kill chain may be disrupted by affectingcompletion of the security related risk use case. Those of skill in theart will recognize that many such embodiments are possible. Accordingly,the foregoing is not intended to limit the spirit, scope, or intent ofthe invention.

In various embodiments, certain user entity behaviors may be observed asthe result of security related activities being enacted during anassociated activity session 910. In various embodiments, certain ofthese user entity behaviors may be considered to be concerningbehaviors, described in greater detail herein. In various embodiments,as described in greater detail herein, certain security relatedactivities associated with each activity session 910 may be processed togenerate a corresponding session fingerprint 912. As likewise describedin greater detail herein, each resulting session fingerprint 912 maythen be associated in certain embodiments with a corresponding securityvulnerability scenario 1914. In certain embodiments, as described ingreater detail herein, individual security vulnerability scenarios 1914may in turn be associated with a particular phase of a cyber kill chain,such as the data theft kill chain phases 2340 shown in FIG. 23.

As an example, as shown in FIG. 23, security related activities enactedby a user entity during activity session(s) ‘A’ 2310 may be processed togenerate session fingerprint(s) ‘A’ 2320, which may then be associatedwith security vulnerability scenario ‘A’ 2330. In turn, securityvulnerability scenario ‘A’ 2330 may in turn be associated with the datareconnaissance 2350 phase of the data theft kill chain 2340. In thisexample, the security related activities may include external searches,application searching, share searching and mapping, internal searches,and so forth.

As another example, security related activities enacted by a user entityduring activity session(s) ‘B’ 2312 may be processed to generate sessionfingerprint(s) ‘B’ 2322, which may then be associated with securityvulnerability scenario ‘B’ 2332. In turn, security vulnerabilityscenario ‘B’ 2332 may in turn be associated with the data access 2352phase of the data theft kill chain 2340. In this example, the securityrelated activities may include cloud access and internal applicationrequests, data repository access, file sharing attempts, and so forth.

As yet another example, security related activities enacted by a userentity during activity session(s) ‘C’ 2314 may be processed to generatesession fingerprint(s) ‘C’ 2324, which may then be associated withsecurity vulnerability scenario ‘C’ 2334. In turn, securityvulnerability scenario ‘C’ 2334 may in turn be associated with the datacollection 2354 phase of the data theft kill chain 2340. In thisexample, the security related activities may include cloud and webdownloads, email and chat attachments, data downloads from applications,data repositories, and file shares, and so forth.

As yet still another example, security related activities enacted by auser entity during activity session(s) ‘D’ 2316 may be processed togenerate session fingerprint(s) ‘D’ 2326, which may then be associatedwith security vulnerability scenario ‘D’ 2336. In turn, securityvulnerability scenario ‘D’ 2336 may in turn be associated with the datastockpiling 2356 phase of the data theft kill chain 2340. In thisexample, the security related activities may include creation of newfolders or cloud sites, uploads to data repositories, creation of newarchives, endpoints, file shares, or network connections, and so forth.

As an additional example, security related activities enacted by a userentity during activity session(s) ‘E’ 2318 may be processed to generatesession fingerprint(s) ‘E’ 2328, which may then be associated withsecurity vulnerability scenario ‘E’ 2338. In turn, securityvulnerability scenario ‘E’ 2338 may in turn be associated with the dataexfiltration 2358 phase of the data theft kill chain 2340. In thisexample, the security related activities may include flash drive andfile transfers, sharing links in cloud applications, external orweb-based email communication, connection to Internet of Things (IoT)infrastructures, and so forth.

In various embodiments, certain phases of a cyber kill chain, such asthe phase of the data theft kill chain 2340 shown in FIG. 3, may beassociated with a corresponding security risk persona, such as the datatheft security risk personas 2360, likewise shown in FIG. 23. Forexample, the data reconnaissance 2350, data access 2352, data collection2354, data stockpiling 2356, and data exfiltration 2358 phases of thedata theft kill chain 2340 may respectively be associated with thewanderer 2372, boundary pusher 2374, hoarder/collector 2376, stockpile2378, and exfiltrator 2380 data theft security risk personas 2360.Accordingly, a security risk persona may be implemented in certainembodiments to align with a corresponding cyber kill chain phase. As anexample, the data theft security risk persona 2360 of wanderer 2372 maybe aligned with the data theft kill chain phase 2340 of datareconnaissance 2350.

In certain embodiments, a security risk persona may be named, stylized,or otherwise implemented, to convey a sense of the general behavioraltheme of an associated user entity. As an example, the data theftsecurity risk persona 2360 of boundary pusher 2374 may convey,characterize, or otherwise represent a concerning behavioral pattern ofa particular user entity attempting to access certain proprietary data.Accordingly, the data theft security risk persona 2360 of boundarypusher 2374 may implemented to align with the data theft kill chain 2340phase of data access 2352.

In certain embodiments, a security risk persona may be implemented todescribe, or otherwise indicate, an associated user entity's behavior ata particular point in time, and by extension, provide an indication oftheir motivation, or intent, or both. Accordingly, a security riskpersona may be implemented in certain embodiments to provide anindication of a user entity's current phase in a particular cyber killchain, or possibly multiple cyber kill chains. In certain embodiments,the alignment of a security risk persona to a corresponding phase of acyber kill chain may be used to accurately map a particular securityrisk persona to concerning behaviors in a way that clarifies cyber killchain phases and associated security vulnerability scenarios 1914. Incertain embodiments, a user entity may exhibit multiple types, orcategorizations, of concerning behaviors, such that they behaviorallyand historically align with multiple security risk personas over thesame interval of time.

In certain embodiments, the ordering of security risk personas mayindicate a progression of the severity of the concerning behaviorsassociated with each phase of a cyber kill chain. In certainembodiments, a user entity's associated security risk persona maytraverse a particular cyber kill chain over time. In certainembodiments, such traversal of a cyber kill chain may be non-linear, orbi-directional, or both. In certain embodiments, tracking the transitionof a user entity's associated security risk persona over time mayprovide an indication of their motivation, or intent, or both.

As an example, as shown in FIG. 23, the data theft security persona 2360of a user entity may initially be that of a hoarder/collector 2376. Inthis example, the user entity's data theft security persona 2360 maytransition to boundary pusher 2374 and wanderer 2372. Alternatively, theuser entity's data theft security persona 2360 may transition directlyto wanderer 2372, and from there, to boundary pusher 2374. As yetanother alternative, the user entity's data theft security persona 2360may transition directly to exfiltrator 2380, and so forth. Skilledpractitioners of the art will recognize that many such possibilities ofa security risk persona traversing a cyber kill chain are possible.Accordingly, the foregoing is not intended to limit the spirit, scope,or intent of the invention.

FIG. 24 shows concerning behaviors related to a contextual risk personaassociated with a corresponding single-phase kill chain implemented inaccordance with an embodiment of the invention. As used herein, acontextual risk persona 2412 broadly refers to a group of user entitybehaviors of interest 2416, associated with a common theme, whoseenactment is not explicitly related to a malicious objective. In variousembodiments, user entity behaviors of interest 2416 associated with acontextual risk persona may not be outcome-oriented. Accordingly, incertain of these embodiments, a contextual risk persona 2412 may beimplemented as a single-phase cyber kill chain.

In certain embodiments, a contextual risk persona 2412 may be typifiedby a descriptor characterizing its associated user entity behaviors ofinterest 2416 In various embodiments, user entity behaviors of interest2416 associated with a particular contextual risk persona 2412 may occurduring a certain period of time 2408. In various embodiments, acontextual risk persona's 2412 associated user entity behaviors ofinterest 2416 may correspond to the occurrence of certain events 2410 atcertain points of time 2408.

As an example, as shown in FIG. 24, a user entity may exhibit a group ofuser entity behaviors of interest 2416 typically associated with thecontextual risk persona 2412 of “Leaver” 2414. To continue the example,the group of user entity behaviors of interest 2416 may include thecreation of multiple resume variations, updating of social mediaprofiles, researching companies via their websites, searching for realestate in a new location, increased documentation of work product, andso forth. Likewise, associated events 2410 may include preparing for ajob search, searching, applying, and interviewing for a new job,following up with a potential employer, negotiating compensation,reviewing an offer, completing a background check, tendering notice, andleaving the organization.

Accordingly, certain embodiments of the invention reflect anappreciation that not all user entity behaviors of interest 2416 areconcerning behaviors, described in greater detail herein. Furthermore,certain embodiments reflect an appreciation that such user entitybehaviors may include simple mistakes or inadvertently risky activities,and as such, are not explicitly linked to a malicious objective. Variousembodiments of the invention likewise reflect an appreciation that whilecertain user entity behaviors of interest 2416 not explicitly beconcerning behaviors, they may represent some degree of security risk.

In various embodiments, certain contextual modifiers 1920 may beimplemented to act as security risk “force multipliers,” by encouragingor accelerating the formation of a particular contextual risk persona2412. In various embodiments, as described in greater detail herein,such contextual modifiers 1920 may include certain stressor 1922 andorganizational 1924 contextual modifiers 1920. For example, as shown inFIG. 24, professional 2006 stressor 1922 contextual modifiers 1920 mayinclude an unsatisfactory performance reviews or compensation, co-workerfriction, unmet expectations, cancelled projects, and so forth. Personal2004 stressor 1922 contextual modifiers 1920 may likewise includecertain life changes, such as health issues, the birth, death, orsickness of a family member or friend, separation or divorce, relocationto a new locale, and so forth. Likewise, organizational 1924 contextualmodifiers 1920 may include changes in management, reorganizations,mergers, acquisitions, downsizing, site closures, changes in workpolicies, declines in stock prices, and so forth. Those of skill in theart will recognize that many such examples are possible. Accordingly,the foregoing is not intended to limit the spirit, scope, or intent ofthe invention.

FIGS. 25a and 25b show tables containing human-centric risk model dataused to generate a user entity risk score associated with a securityvulnerability scenario implemented in accordance with an embodiment ofthe invention for an example data theft scenario. In certainembodiments, as described in greater detail herein, security riskpersonas 1908 associated with a particular security vulnerabilityscenario 1914 may be assigned a median risk score 1910. For example, asshown in FIGS. 25a and 25b , a security risk persona 1908 of “BoundaryPusher” may be assigned a median risk score 1910 of ‘20’ for thesecurity vulnerability scenario 1914 of “Data Theft.”

In certain embodiments, a concerning behavior risk score 1918 may begenerated for a particular user entity exhibiting concerning behaviorstypically associated with a corresponding security risk persona 1908during a calendar interval 2520 of time. In certain embodiments, theconcerning behavior risk score 1918 may be adjusted according to theuser entity's predisposition 1912 and the median risk score 1910corresponding to security risk persona 1908 currently associated withthe user entity. In certain embodiments, certain contextual modifiers1920 may be applied to the concerning behavior risk score 1918 togenerate an associated user entity risk score 1930.

For example, as shown in FIG. 25b , a user entity may be exhibitingconcerning behaviors associated with a security risk persona 1908 of“Hoarder/Collector” associated with a security vulnerability scenario1914 of “Data Theft” during the calendar interval 2520 of the secondhalf of July. In this example, the user entity's predisposition 1912 is“Hoarder/Collector,” signifying that the user entity's long-termpredisposition is to collect and hoard information. Accordingly, themedian risk score 1910 of ‘20’ associated with the security risk persona1908 of “Hoarder/Collector” may be used to generate a concerningbehavior risk score 1918 of ‘30’. To continue the example, the resultingconcerning behavior risk score 1918 of ‘30’ is then respectivelyadjusted by stressor 1922, organizational 1924, and motivation 1926contextual modifiers 1920 to yield a user entity risk score 1930 of‘49’.

FIG. 26 shows a user interface (UI) window implemented in accordancewith an embodiment of the invention to graphically display an entityrisk score as it changes over time. In certain embodiments, changes in auser entity's entity risk score 1930 over an interval of time, such ascalendar intervals 2520, may be displayed within a window of a graphicaluser interface (GUI) 2602, such as that shown in FIG. 26.

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a method, system, or computer program product.Accordingly, embodiments of the invention may be implemented entirely inhardware, entirely in software (including firmware, resident software,micro-code, etc.) or in an embodiment combining software and hardware.These various embodiments may all generally be referred to herein as a“circuit,” “module,” or “system.” Furthermore, the present invention maytake the form of a computer program product on a computer-usable storagemedium having computer-usable program code embodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), anoptical storage device, or a magnetic storage device. In the context ofthis document, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java, Smalltalk, C++ or the like. However, the computer program codefor carrying out operations of the present invention may also be writtenin conventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Embodiments of the invention are described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The present invention is well adapted to attain the advantages mentionedas well as others inherent therein. While the present invention has beendepicted, described, and is defined by reference to particularembodiments of the invention, such references do not imply a limitationon the invention, and no such limitation is to be inferred. Theinvention is capable of considerable modification, alteration, andequivalents in form and function, as will occur to those ordinarilyskilled in the pertinent arts. The depicted and described embodimentsare examples only, and are not exhaustive of the scope of the invention.

Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

What is claimed is:
 1. A computer-implementable method for performing asecurity operation, comprising: monitoring an entity, the monitoringobserving at least one electronically-observable data source; derivingan observable based upon the monitoring of the electronically-observabledata source; identifying a security related activity of the entity, thesecurity related activity being based upon the observable derived fromthe electronic data source, the security related activity being ofanalytic utility; associating the security related activity with a phaseof a cyber kill chain; and, performing a security operation on thesecurity related activity via a security system, the security operationdisrupting performance of the phase of the cyber kill chain.
 2. Themethod of claim 1, further comprising: associating a security riskpersona with the entity; and, generating a baseline risk score basedupon the security risk persona.
 3. The method of claim 2, wherein: thedisrupting performance of the component of the cyber kill chain is basedupon the security risk persona.
 4. The method of claim 2, wherein: thesecurity risk persona characterizes a user entity behavior.
 5. Themethod of claim 4, wherein: the cyber kill chain comprises an associatedsecurity vulnerability scenario.
 6. The method of claim 5, wherein: thephase of the cyber kill chain comprises a risk use case; and,performance of the component of the cyber kill chain is disrupted byaffecting completion of the risk use case.
 7. A system comprising: aprocessor; a data bus coupled to the processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: monitoring an entity, the monitoringobserving at least one electronically-observable data source; derivingan observable based upon the monitoring of the electronically-observabledata source; identifying a security related activity of the entity, thesecurity related activity being based upon the observable derived fromthe electronic data source, the security related activity being ofanalytic utility; associating the security related activity with a phaseof a cyber kill chain; and, performing a security operation on thesecurity related activity via a security system, the security operationdisrupting performance of the phase of the cyber kill chain.
 8. Thesystem of claim 7, wherein the instructions executable by the processorare further configured for: associating a security risk persona with theentity; and, generating a baseline risk score based upon the securityrisk persona.
 9. The system of claim 8, wherein: the disruptingperformance of the component of the cyber kill chain is based upon thesecurity risk persona.
 10. The system of claim 8, wherein: the securityrisk persona characterizes a user entity behavior.
 11. The system ofclaim 10, wherein: the cyber kill chain comprises an associated securityvulnerability scenario.
 12. The system of claim 11, wherein: the phaseof the cyber kill chain comprises a risk use case; and, performance ofthe component of the cyber kill chain is disrupted by affectingcompletion of the risk use case.
 13. A non-transitory, computer-readablestorage medium embodying computer program code, the computer programcode comprising computer executable instructions configured for:monitoring an entity, the monitoring observing at least oneelectronically-observable data source; deriving an observable based uponthe monitoring of the electronically-observable data source; identifyinga security related activity of the entity, the security related activitybeing based upon the observable derived from the electronic data source,the security related activity being of analytic utility; associating thesecurity related activity with a phase of a cyber kill chain; and,performing a security operation on the security related activity via asecurity system, the security operation disrupting performance of thephase of the cyber kill chain.
 14. The non-transitory, computer-readablestorage medium of claim 13, wherein the computer executable instructionsare further configured for: associating a security risk persona with theentity; and, generating a baseline risk score based upon the securityrisk persona.
 15. The non-transitory, computer-readable storage mediumof claim 14, wherein: the disrupting performance of the component of thecyber kill chain is based upon the security risk persona.
 16. Thenon-transitory, computer-readable storage medium of claim 14, wherein:the security risk persona characterizes a user entity behavior.
 17. Thenon-transitory, computer-readable storage medium of claim 16, wherein:the cyber kill chain comprises an associated security vulnerabilityscenario.
 18. The non-transitory, computer-readable storage medium ofclaim 17, wherein: the phase of the cyber kill chain comprises a riskuse case; and, performance of the component of the cyber kill chain isdisrupted by affecting completion of the risk use case.
 19. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the computer executable instructions are deployable to a client systemfrom a server system at a remote location.
 20. The non-transitory,computer-readable storage medium of claim 13, wherein: the computerexecutable instructions are provided by a service provider to a user onan on-demand basis.